Method and apparatus for generating conversation content for language and vocabulary learning training

Abstract

This invention discloses a method and apparatus for generating conversational content for language and vocabulary learning training, relating to the field of artificial intelligence technology. The specific solution includes: a computer device acquiring at least one target word; using the at least one target word for user sentence-making training; and, in response to user input, acquiring user-input conversational content; and generating conversational content based on the user-input conversational content and the at least one target word. The conversational content guides the user to use the at least one target word in the conversation. This invention, through conversational content generated based on user-input conversational content and the at least one target word, automatically engages in conversation with the user, thereby creating a realistic and interactive language communication environment and guiding the user to practice sentence-making using the at least one target word. This helps the user more effectively master the usage of the at least one target word and improves the efficiency of vocabulary learning.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and in particular to a method and apparatus for generating conversational content for language and vocabulary learning training. Background Technology

[0002] In language learning, students often don't have a strong impression of newly learned words or are unfamiliar with their usage. To deepen their understanding and master the usage of new words, students can learn them through interactive sentence-making exercises.

[0003] However, existing human-computer interaction methods are relatively simple and lack contextual interaction, resulting in low efficiency in word learning. Summary of the Invention

[0004] This invention provides a method and apparatus for generating conversational content for language and vocabulary learning training, which solves the problems of simple word learning formats, lack of contextual interaction, and low efficiency in word learning.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] In a first aspect, the present invention provides a method for generating conversational content for language and vocabulary learning training, the method comprising:

[0007] Obtain at least one target word, which is used for user sentence construction training;

[0008] Responding to user input, obtain the content of the user input session;

[0009] The conversation content is generated based on user input and at least one target word; the conversation content is used to guide the user to use at least one target word in the conversation.

[0010] In conjunction with the first aspect, in one possible implementation, the user input conversation content includes at least one of the following: input sentence, input voice data, wherein the input voice data corresponds to the input sentence; the conversation content includes at least one of the following: conversation sentence, conversation voice data, conversation image, conversation video; wherein, the conversation voice data is data obtained by converting the conversation sentence into speech, the content of the conversation image includes the conversation sentence, and the conversation video is a video generated based on the conversation sentence.

[0011] In conjunction with the first aspect, in one possible implementation, generating session content based on user-input session content and at least one target word includes: determining M candidate session content that match the user-input session content, where M is a positive integer; when M equals 1, determining the candidate session content as the session content; when M is greater than 1, determining related words based on at least one target word, and determining the degree of relevance between the related words and each candidate session content, and determining the candidate session content corresponding to the maximum degree of relevance among the M degrees of relevance as the session content; wherein, the related words include words that are semantically identical or similar to words in at least one target word, and the degree of relevance is used to indicate the semantic similarity between the related words and each candidate session content.

[0012] In conjunction with the first aspect, in one possible implementation, determining the relevance of the associated words to each candidate session content includes: obtaining keywords in each candidate session content based on the part of speech of the words included in each candidate session content; determining the relevance of each associated word to each candidate session content based on the relevance of each associated word to each keyword included in each candidate session content; and determining the relevance of the associated words to each candidate session content based on the relevance of each associated word to each candidate session content.

[0013] In conjunction with the first aspect, in one possible implementation, determining M candidate conversation contents that match the user input conversation content includes: determining the similarity between the user input conversation content and each sentence in a preset sentence library; determining sentences in the preset sentence library with a similarity greater than a preset threshold as candidate conversation contents, thus obtaining M candidate conversation contents.

[0014] In conjunction with the first aspect, in one possible implementation, the method for generating conversational content for language and vocabulary learning training further includes: determining the complexity of the user-input conversational content based on the number of keywords and grammatical structure in the user-input conversational content; obtaining the number of times a scoring word appears in the user-input conversational content, wherein the scoring word is a word from at least one target word; and determining the evaluation parameters corresponding to the user-input conversational content based on the complexity of the user-input conversational content and the number of times the scoring word appears in the user-input conversational content.

[0015] In conjunction with the first aspect, in one possible implementation, the method for generating conversational content for language and vocabulary learning training further includes: after obtaining the user's input conversational content, increasing the recorded number of sentence constructions by a preset value; when the number of sentence constructions after increasing the preset value equals the preset number, outputting a prompt message in response to the user's end-of-conversation operation, the prompt message prompting the user to share the conversation; and transmitting the conversation to a sharing platform in response to the user's confirmation operation of the prompt message.

[0016] In conjunction with the first aspect, in one possible implementation, the conversation content includes conversational sentences and conversational speech data. The method for generating conversational content for language and vocabulary learning training also includes: outputting conversational speech data, which is used to guide the user's speech training; and after outputting the conversational speech data, displaying the conversational sentences.

[0017] In conjunction with the first aspect, in one possible implementation, the method for generating conversation content for language and vocabulary learning training further includes: in response to a user's translation operation on the conversation, converting the user-input conversation content into first data of a preset language type, converting the conversation content into second data of a preset language type, and displaying the first data and the second data in the dialogue interface.

[0018] In conjunction with the first aspect, in one possible implementation, acquiring at least one target word includes: acquiring at least one target word in response to a user's input of a word in the dialogue interface; or, acquiring target speech data in response to a user's voice input in the dialogue interface, and acquiring at least one target word based on the target speech data; or, displaying a word library button in the dialogue interface, and displaying a word list in response to a user's operation on the word library button, the word list including words from a preset word library; acquiring at least one target word in response to a user's selection operation in the word list; or, acquiring word learning data in response to a word training instruction, and using words within a preset time period from the word learning data as at least one target word.

[0019] Secondly, the present invention provides an apparatus for generating conversational content for language and vocabulary learning training, the apparatus comprising:

[0020] The acquisition module is used to acquire at least one target word, which is used for user sentence construction training; in response to user input, it acquires the user input conversation content.

[0021] The generation module is used to generate conversation content based on user-input conversation content and at least one target word; the conversation content is used to guide the user to use at least one target word in the conversation.

[0022] In conjunction with the second aspect, in one possible implementation, the user input conversation content includes at least one of the following: an input sentence and input voice data, wherein the input voice data corresponds to the input sentence; the conversation content includes at least one of the following: a conversation sentence, conversation voice data, a conversation image, and a conversation video; wherein the conversation voice data is data obtained by converting the conversation sentence into speech, the content of the conversation image includes the conversation sentence, and the conversation video is a video generated based on the conversation sentence.

[0023] In conjunction with the second aspect, in one possible implementation, the generation module is specifically used to: determine M candidate conversation contents that match the user-input conversation content, where M is a positive integer; when M equals 1, determine the candidate conversation contents as the conversation content; when M is greater than 1, determine related words based on at least one target word, and determine the degree of relevance between the related words and each candidate conversation content, and determine the candidate conversation content corresponding to the maximum degree of relevance among the M relevance values ​​as the conversation content; wherein, the related words include words that are semantically the same or similar to words in at least one target word, and the degree of relevance is used to indicate the semantic similarity between the related words and each candidate conversation content.

[0024] In conjunction with the second aspect, in one possible implementation, the generation module is specifically used to: obtain keywords in each candidate session content based on the part-of-speech of the words included in each candidate session content; determine the relevance of each associated word to each candidate session content based on the relevance of each associated word to each keyword included in each candidate session content; and determine the relevance of each associated word to each candidate session content based on the relevance of each associated word to each candidate session content.

[0025] In conjunction with the second aspect, in one possible implementation, the generation module is specifically used to: determine the similarity between the user-input conversation content and each sentence in the preset sentence library; determine the sentences in the preset sentence library with a similarity greater than a preset threshold as candidate conversation content, and obtain M candidate conversation content.

[0026] In conjunction with the second aspect, in one possible implementation, the device for generating conversational content for language and vocabulary learning training further includes an evaluation module. The evaluation module is used to: determine the complexity of the user-input conversational content based on the number of keywords and grammatical structure in the user-input conversational content. The acquisition module is also used to acquire the number of times a scoring word appears in the user-input conversational content, where the scoring word is a word from at least one target word. The evaluation module is also used to determine evaluation parameters corresponding to the user-input conversational content based on the complexity of the user-input conversational content and the number of times the scoring word appears in the user-input conversational content.

[0027] In conjunction with the second aspect, in one possible implementation, the device for generating conversational content for language and vocabulary learning training further includes an increment module, an output module, and a sharing module. The increment module is used to: after acquiring user-inputted conversational content, increment the recorded number of sentence creations by a preset value. The output module is used to, in response to the user's end-of-conversation operation, output a prompt message if the number of sentence creations after incrementing the preset value equals the preset number, prompting the user to share the conversation. The sharing module is used to, in response to the user's confirmation of the prompt message, transmit the conversation to a sharing platform.

[0028] In conjunction with the second aspect, in one possible implementation, the conversation content includes conversational sentences and conversational voice data. The output module is further configured to: output conversational voice data, which is used to guide the user's voice training; and after outputting the conversational voice data, display the conversational sentences.

[0029] In conjunction with the second aspect, in one possible implementation, the device for generating conversational content for language and vocabulary learning training further includes a translation module, which is used to: in response to a user's translation operation on the conversation, convert the user-input conversational content into first data of a preset language type, convert the conversational content into second data of a preset language type, and display the first data and the second data in the dialogue interface.

[0030] In conjunction with the second aspect, in one possible implementation, the acquisition module is specifically used to: acquire at least one target word in response to the user's input of words in the dialogue interface; or, acquire target speech data in response to the user's voice input in the dialogue interface, and acquire at least one target word based on the target speech data; or, display a word library button in the dialogue interface, and in response to the user's operation on the word library button, display a word list in the dialogue interface, the word list including words from a preset word library; acquire at least one target word in response to the user's selection operation in the word list; or, acquire word learning data in response to a word training instruction, and use words within a preset time period in the word learning data as at least one target word.

[0031] Thirdly, the present invention provides a computer device comprising a processor and a memory. The memory stores computer program code, which includes computer instructions. When the processor executes the computer instructions, the computer device performs a method for generating conversational content for language and vocabulary learning training, as described in the first aspect and any possible implementation thereof.

[0032] Fourthly, the present invention provides a computer-readable storage medium having computer instructions stored thereon, which, when executed on a computer device, cause the computer device to perform a method for generating conversational content for language and vocabulary learning training, as described in the first aspect or any possible implementation thereof.

[0033] This invention provides a method for generating conversational content for language and vocabulary learning training. A computer device acquires at least one target word for user sentence-making training. Responding to user input, the device acquires user-input conversational content and generates conversational content based on the user-input conversational content and the at least one target word. This conversational content guides the user to use the at least one target word in the conversation. By generating conversational content based on user-input conversational content and the at least one target word, this invention automatically engages in conversation with the user, creating a realistic and interactive language communication environment. It guides the user to practice sentence-making using the at least one target word, helping the user more effectively master its usage and improving vocabulary learning efficiency. Attached Figure Description

[0034] Figure 1 A schematic diagram illustrating the application environment of a method for generating conversational content for language and vocabulary learning training, provided in an embodiment of the present invention.

[0035] Figure 2 This is one of the flowcharts illustrating a method for generating conversational content for language and vocabulary learning training, provided by an embodiment of the present invention.

[0036] Figure 3 One of the schematic diagrams of a dialog interface provided in an embodiment of the present invention;

[0037] Figure 4 A second schematic diagram of a dialog interface provided in an embodiment of the present invention;

[0038] Figure 5 A third schematic diagram of a dialog interface provided in an embodiment of the present invention;

[0039] Figure 6 A fourth schematic diagram of a dialog interface provided in an embodiment of the present invention;

[0040] Figure 7 This is a second flowchart illustrating a method for generating conversational content for language and vocabulary learning training, provided by an embodiment of the present invention.

[0041] Figure 8 The third flowchart illustrates a method for generating conversational content for language and vocabulary learning training, as provided in an embodiment of the present invention.

[0042] Figure 9 The fourth flowchart illustrates a method for generating conversational content for language and vocabulary learning training, as provided in an embodiment of the present invention.

[0043] Figure 10The fifth flowchart illustrates a method for generating conversational content for language and vocabulary learning training, as provided in an embodiment of the present invention.

[0044] Figure 11 Fifth schematic diagram of a dialog interface provided in an embodiment of the present invention;

[0045] Figure 12 This is a sixth flowchart illustrating a method for generating conversational content for language and vocabulary learning training, provided as an embodiment of the present invention.

[0046] Figure 13 A sixth schematic diagram of a dialog interface provided in an embodiment of the present invention;

[0047] Figure 14 This is one of the schematic diagrams of a conversation content generation device for language and vocabulary learning training provided in an embodiment of the present invention;

[0048] Figure 15 A second schematic diagram of a conversation content generation device for language and vocabulary learning training provided in an embodiment of the present invention;

[0049] Figure 16 This is the third schematic diagram of a conversation content generation device for language and vocabulary learning training provided in an embodiment of the present invention. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of embodiments of this disclosure, unless otherwise stated, "a plurality of" means two or more. Furthermore, the use of "based on" or "according to" implies openness and inclusiveness, because processes, steps, calculations, or other actions "based on" or "according to" one or more of the stated conditions or values ​​may in practice be based on additional conditions or beyond the stated values.

[0052] To address the problems of simple word learning formats, lack of contextual interaction, and low efficiency in word learning, this invention provides a method and apparatus for generating conversational content for language and vocabulary learning training. A computer device acquires at least one target word, which is used for user sentence construction training. In response to user input, the device acquires user-input conversational content and generates conversational content based on the user-input conversational content and at least one target word. The conversational content guides the user to use at least one target word in the conversation.

[0053] Thus, this embodiment of the invention automatically engages in conversation with the user based on the user-input conversation content and at least one target word, thereby creating a realistic and interactive language communication environment and guiding the user to practice sentence construction using at least one target word. This helps the user to more effectively master the usage of at least one target word and improves the efficiency of word learning.

[0054] The execution entity of the method for generating conversational content for language and vocabulary learning training provided in this embodiment of the invention is a device for generating conversational content for language and vocabulary learning training. This device can be a computer device, a processor of the computer device, or a client installed on the computer device. In this embodiment of the invention, the method for generating conversational content for language and vocabulary learning training executed by a computer device is described as an example.

[0055] In one scenario, the computer device can be a terminal device, which can execute the method for generating conversational content for language and vocabulary learning training according to embodiments of the present invention. For example, the terminal device can be a mobile phone, tablet computer, laptop computer, or other similar devices.

[0056] In another scenario, the computer device can be a terminal device or a server, and the terminal device and the server can work together to complete the method for generating conversational content for language and vocabulary learning training according to embodiments of the present invention. The server can be a single server, a server cluster, or a cloud platform computing center. When the server is a server cluster, the different servers in the cluster can provide different services to the terminal device, such as speech recognition and text matching.

[0057] Figure 1 This is a schematic diagram illustrating an application environment for a possible method of generating conversational content for language and vocabulary learning training, provided by an embodiment of the present invention. For example... Figure 1As shown, this scenario may include a terminal device 10, a speech recognition server 11, and a text matching server 12. The terminal device 10 communicates with the speech recognition server 11 and the text matching server 12 via wired or wireless means, respectively.

[0058] Terminal device 10 is configured to acquire at least one target word and, in response to a user's input operation, acquire user-input conversation content. Terminal device 10 is also configured to generate conversation content based on the user-input conversation content and at least one target word. The user-input conversation content includes at least one of the following: an input sentence or input voice data. The conversation content includes at least one of the following: a conversation sentence, conversation voice data, a conversation image, or a conversation video.

[0059] For example, when the user's input operation is voice input, the user input session content obtained by the terminal device 10 is the input voice data. The terminal device 10 is also used to send a voice recognition request containing sentence voice data to the voice recognition server 11.

[0060] The speech recognition server 11 is used to recognize the speech data of a sentence, obtain the corresponding input sentence, and return the input sentence to the terminal device 10.

[0061] Terminal device 10 is further configured to send user-input session content in a text matching request to text matching server 12. Text matching server 12 is configured to determine candidate session content that matches the user-input session content. It is also configured to determine session content from M candidate session content when M is greater than 1, and send the session content to terminal device 10. Terminal device 10 is configured to output the received session content.

[0062] Based on the above description of the application environment of the method for generating conversational content for language and vocabulary learning training, this embodiment of the invention provides a method for generating conversational content for language and vocabulary learning training. For example... Figure 2 As shown, the method for generating conversational content for language and vocabulary learning training may include the following steps S201-S203.

[0063] It should be noted that at least one target word involved in the embodiments of the present invention can be a word of any language type. For example, the target word can be an English word, a Chinese word, or a French word; the embodiments of the present invention do not impose specific limitations. For ease of understanding, the embodiments of the present invention use an English word as the target word, that is, they illustrate the invention with examples of sentence-making exercises using English words.

[0064] S201. The terminal device acquires at least one target word, and the at least one target word is used for sentence construction training.

[0065] The terminal device has a sentence-making training application installed. When the user needs to practice making sentences using multiple words through this application, the user can open the application. The terminal device can respond to the user's operation, display a dialog interface, and retrieve at least one target word from this interface. This at least one target word can be a word entered by the user, a word recently learned by the user, or a word selected by the user from a preset word library.

[0066] Optionally, the terminal device may use the following methods to obtain at least one target word. This embodiment of the invention does not limit the specific method of obtaining at least one target word.

[0067] In one possible implementation, when a user enters at least one target word in the dialogue interface of the sentence-making training application, the terminal device can respond to the user's input operation in the dialogue interface and obtain at least one target word.

[0068] In another possible implementation, the terminal device may respond to the user's voice input operation in the dialogue interface, acquire target voice data, and acquire at least one target word based on the target voice data.

[0069] Optionally, the terminal device can obtain at least one target word based on the target speech data by recognizing the target speech data as at least one target word using a pre-stored speech recognition model. Alternatively, the terminal device can send a speech recognition request containing the target speech data to a speech recognition server, so that the speech recognition server can recognize the target speech data as at least one target word and return it to the terminal device. The terminal device can then receive at least one target word sent by the speech recognition server.

[0070] In another possible implementation, a dictionary button is displayed in the dialog interface. When the user clicks the dictionary button, the terminal device responds to the user's action by displaying a word list in the dialog interface. This word list includes words from a preset dictionary. The terminal device can then retrieve at least one target word based on the user's selection from the word list.

[0071] In another possible implementation, the terminal device can respond to a word training instruction, acquire word learning data, and use words within a preset time period from the word learning data as at least one target word. Specifically, the terminal device can respond to a user's action of opening a sentence-making training application, display a dialog interface, acquire word learning data, use words within a preset time period from the word learning data as at least one target word, and display the at least one target word in the dialog interface.

[0072] It is understandable that the aforementioned vocabulary learning data could be data from another vocabulary learning application installed on the terminal device. This data could include English words recently learned by the user. After opening the sentence-making training application, the terminal device could use at least one recently learned English word from the vocabulary learning data as at least one target word. Alternatively, the aforementioned vocabulary learning data could be historical data from the sentence-making training application. This data could include English words recently used by the user in sentence-making. After opening the sentence-making training application, the terminal device could use at least one English word from the historical data that the user had a poor grasp of as at least one target word. Of course, the vocabulary learning data could also be other types of data, as long as it includes multiple words.

[0073] This embodiment provides a variety of ways to obtain at least one target word, enabling users to conduct targeted word training conveniently and as needed, thus improving the flexibility of word training.

[0074] For example, when a user opens the sentence-making training application, the terminal device can respond to the user's opening action by displaying an initial dialogue interface and retrieving word learning data. If the five most recently learned words obtained from the word learning data include: again, school, communication, nice, and slow, then... Figure 3 As shown, the terminal device can pop up a sentence-making prompt box in the initial dialogue interface. The sentence-making prompt box includes sentence-making prompt information in the style of "Please use the following words to make a sentence and talk to me: again, school, communication, nice, slow", as well as buttons in the style of "Chat casually" and "OK".

[0075] When the user clicks the "OK" button, the terminal device can respond to the user's action on the "OK" button by closing the sentence-making prompt box and displaying, as shown below. Figure 4 The dialog interface shown. Figure 4 As shown, the terminal device can display a "√" before the button in the dialogue interface, such as the "Word Sentence Construction Mode," to indicate that the current dialogue interface has entered the word training state. Figure 4 As shown, the terminal device can display an opening sentence in the dialogue interface, such as "I am pleased to introduce myself to you, Tom," thus prompting and guiding the user to begin sentence-making practice. Figure 4 As shown, the dialog interface can also include prompts for sentence-making words, such as "sentence-making words: again, school, communication, nice, slow", to remind users to use the words in the prompts when making sentences.

[0076] S202. The terminal device responds to the user's input operation and obtains the user's input session content.

[0077] The input operation can be either voice input or text input.

[0078] Optionally, the user input conversation content may include at least one of the following: input sentence, input voice data. The input voice data corresponds to the input sentence.

[0079] Optionally, when a user performs voice input in the dialogue interface, the terminal device can respond to the user's voice input by acquiring the user's voice data and converting it into a corresponding input sentence using speech recognition technology. Alternatively, after acquiring the user's voice data, the terminal device can send a speech recognition request containing the voice data to a speech recognition server and receive the speech recognition result, i.e., the input sentence, from the speech recognition server.

[0080] Optionally, when a user performs text input on the dialog interface, the terminal device can respond to the user's text input on the dialog interface and directly obtain the input sentence.

[0081] It is understandable that the input sentence mentioned above can be a sentence that the user creates based on at least one target word and then inputs into the dialog interface.

[0082] For example, consider voice input. Combined with... Figure 4 When users need to perform voice input, they can click on the following: Figure 4 The dialogue interface shown includes a voice input button. The terminal device can receive a user's click on this voice input button. In response to this action, such as... Figure 5 As shown, the terminal device can pop up a voice input box in the dialogue interface and start recording. Then, in response to the user's operation of the "Finish" button in the voice input box, the recording ends, the input voice data is obtained, and the input sentence corresponding to the input voice data is obtained.

[0083] In addition, such as Figure 5 As shown, a text input box can also be displayed in the dialog interface. When the user enters a sentence in the text input box and clicks the "send" button after finishing the input, the terminal device can respond to the user's operation on the "send" button in the dialog interface and retrieve the entered sentence from the text input box.

[0084] S203. The terminal device generates conversation content based on user-input conversation content and at least one target word; the conversation content is used to guide the user to use at least one target word in the conversation.

[0085] The conversation content may include at least one of the following: conversation sentences, conversation audio data, conversation images, and conversation videos. Conversation audio data is data obtained by converting conversation sentences into speech; conversation images include conversation sentences in their content; and conversation videos are videos generated based on conversation sentences.

[0086] Understandably, conversation content can be responses based on user input, questions based on user input, or a combination of responses and questions based on user input.

[0087] Optionally, in this embodiment of the invention, the terminal device can determine M candidate session contents that match the user input session, where M is a positive integer. When M equals 1, the terminal device can determine the candidate session content as the session content. When M is greater than 1, the terminal device can determine related words based on at least one target word, and determine the relevance between the related words and each candidate session content, and determine the candidate session content corresponding to the highest relevance among the M relevance values ​​as the session content. The related words include words that are semantically identical or similar to words in at least one target word, and the relevance value is used to indicate the semantic similarity between the related words and each candidate session content.

[0088] Understandably, the higher the semantic similarity, the closer the semantic connection between the two phrases in the text, and the greater the probability that they will appear in the same set of dialogue texts. For example, "How are you" and "I'm fine, thank you" have a very close semantic connection in the text, and there is a high probability that they will appear in the same set of dialogue texts.

[0089] Optionally, if the user inputs a sentence as the input conversation content and the candidate conversation content consists of candidate conversation sentences, the terminal device can use Natural Language Processing (NLP) technology to determine M candidate conversation sentences that match the input sentence from a preset sentence database. Alternatively, the terminal device can send a text matching request containing the input sentence to a text matching server, so that the text matching server can determine M candidate conversation sentences that match the input sentence.

[0090] Optionally, in this embodiment of the invention, in addition to the terminal device itself determining the relevance between at least one target word and each candidate conversation sentence, the server may also determine the relevance between at least one target word and each candidate conversation sentence.

[0091] Optionally, in this embodiment of the invention, in addition to the terminal device determining the conversation sentence itself, the server can also determine the conversation sentence and return it to the terminal device.

[0092] Understandably, on the one hand, the conversational sentences are highly correlated with the input sentences, creating a realistic interactive language communication environment. On the other hand, the conversational sentences are also highly correlated with the K words to be trained, which can continue to guide users to construct sentences using at least one target word.

[0093] Furthermore, the terminal device can output conversation content at a relevant position in the dialog interface where the user inputs conversation content. For example, the terminal device can display the corresponding conversation sentence below the input sentence, or it can display the corresponding conversation sentence at a relative position after a line break in the input sentence. The specific location of the relevant position is not specifically limited here.

[0094] For example, combined Figure 5 ,like Figure 6 As shown, the opening sentence is: "How are you, Tom?" After the user constructs a five-word sentence in the dialogue interface, the terminal device can obtain the input sentence "Keep on growing never giveup" and display it in the dialog box with the line offset to the right. Then, based on the input sentence, the terminal device matches candidate conversation sentences, and after determining the conversation sentence from the candidate conversation sentences, it can display the conversation sentence "Are we still talking about something? By the way, please try to use the words in the box, when chatting with me" in the dialog box with the line offset to the left.

[0095] Understandably, the terminal device can also respond to the user's action on the "Set the minimum number of sentences required to complete the dialogue" button in the dialogue interface, setting the minimum number of sentences required for this round of word learning, such as 5 times. Figure 6 As shown, the dialog interface can display the "number of sentences in this round" and the corresponding current and total number to remind the user of the number of sentences still to be completed.

[0096] This invention provides a method for generating conversational content for language and vocabulary learning training. A terminal device acquires at least one target word for user sentence-making training. Responding to user input, the device acquires user-input conversational content and generates conversational content based on the user-input conversational content and the at least one target word. This conversational content guides the user to use the at least one target word in the conversation. By generating conversational content based on user-input conversational content and the at least one target word, this invention automatically engages in conversation with the user, creating a realistic and interactive language communication environment. It guides the user to practice sentence-making using the at least one target word, helping the user more effectively master its usage and improving vocabulary learning efficiency.

[0097] Optionally, based on the above embodiments, combined with Figure 2 ,like Figure 7 As shown, step S203 above may include:

[0098] S301. The terminal device obtains keywords in each candidate session content based on the part of speech of the words included in each candidate session content.

[0099] The part of speech can include verbs, nouns, prepositions, etc. Words of different parts of speech carry different weights in the semantics of a sentence. For example, keywords can be verbs or nouns from each alternative conversation content; there can be one or more keywords, and the number of keywords is not limited here.

[0100] S302. The terminal device determines the relevance of each associated word to each candidate session content based on the relevance of each associated word to each keyword included in each candidate session content.

[0101] Specifically, computer devices can use NLP technology to calculate the relevance of each related word to each keyword included in each alternative session content.

[0102] It is understandable that the degree of relevance between words and sentences can be represented by the degree of relevance between the words and all the keywords included in the sentence.

[0103] For example, the terminal device can determine the relevance between the associated word and the candidate session content by summing the relevance of the associated word to each keyword included in each candidate session content. Here, there is no limitation on the calculation method of the relevance between the associated word and the candidate session content.

[0104] S303. The terminal device determines the relevance between each associated word and each candidate session content based on the relevance between each associated word and each candidate session content.

[0105] Specifically, the terminal device can determine the relevance of a keyword to a candidate session content by summing the relevance scores of each related keyword to that content, or by averaging the relevance scores of each related keyword to the candidate session content, or by determining the maximum relevance score among all related keywords to the candidate session content. The method for determining the relevance of a keyword to each candidate session content is not limited here.

[0106] Furthermore, the terminal device can also identify any word from at least one target word in the user's input content, and filter out the related words corresponding to these words when calculating the relevance, so that the conversation content is only strongly related to words from at least one target word that the user has not practiced, thereby guiding the user to use words from at least one target word that they have not practiced to make sentences, and further improving the efficiency of word learning.

[0107] In this embodiment, the terminal device obtains keywords in each candidate conversation content based on the part of speech of the words included in each candidate conversation content, and determines the degree of association between each related word and each candidate conversation content based on the degree of association between each related word and each keyword included in each candidate conversation content. This ensures that the degree of association can accurately represent the semantic association between the related word and each candidate conversation content, providing an accurate basis for determining the conversation sentence.

[0108] Optionally, based on the above embodiments, such as Figure 8 As shown, the M candidate conversation contents that match the user input conversation content can specifically include:

[0109] S401. The terminal device determines the similarity between the user's input conversation content and each sentence in the preset sentence library.

[0110] The preset sentence library can store a large number of sentences for active questioning or passive answering, and the alternative conversation content can be sentences from the preset sentence library that are highly relevant to the conversation content entered by the user.

[0111] Specifically, computer devices can use NLP technology to determine the similarity between the input sentence and each sentence in a preset sentence database. The calculation method for similarity is similar to the calculation method for correlation in the above embodiments, and will not be elaborated here.

[0112] S402. The terminal device identifies sentences with a similarity greater than a preset threshold in the preset sentence library as candidate conversation content, and obtains M candidate conversation contents.

[0113] Specifically, the computer device can identify sentences with a similarity greater than a preset threshold as candidate conversation content, resulting in M ​​candidate conversation content. Alternatively, it can sort the similarity scores from highest to lowest and then determine the candidate conversation content according to a preset percentage. For example, sentences from a preset sentence library corresponding to the top 5% similarity scores can be identified as candidate conversation content. The method for selecting candidate conversation content is not limited here.

[0114] In this embodiment, the terminal device determines the similarity between the user-input conversation content and each sentence in the preset sentence library, and identifies sentences in the preset sentence library with a similarity greater than a preset threshold as candidate conversation content, thus obtaining M candidate conversation content. This provides a sample basis for further screening of conversation content, thereby creating a realistic and vivid interactive language training environment, which is conducive to guiding users to practice sentence construction and thus improving the efficiency of word training.

[0115] Optionally, based on the above embodiments, such as Figure 9 As shown, the above-mentioned method for generating conversational content for language and vocabulary learning training also includes:

[0116] S501. The terminal device determines the complexity of the user-input conversation content based on the number of keywords and the grammatical structure in the user-input conversation content.

[0117] Understandably, the complexity of user-input conversation content is related to the number of keywords and the grammatical structure in the user-input conversation content. Generally speaking, the more keywords and the more complex the grammatical structure in the user-input conversation content, the higher the complexity of the user-input conversation content and the higher the quality of the sentences constructed by the user-input conversation content.

[0118] Specifically, the terminal device can determine the complexity of the input sentence based on the number of keywords and grammatical structure in the user's input conversation content.

[0119] S502, The terminal device obtains the number of times the rating words appear in the user-input conversation content, where the rating words are at least one word from the target words.

[0120] Understandably, the more types and quantities of rating words there are, the higher the quality of sentences created by users when inputting conversation content.

[0121] Specifically, the terminal device can obtain the number of times the rating words appear in the user's input conversation content.

[0122] S503. The terminal device determines the evaluation parameters corresponding to the user input conversation content based on the complexity of the user input conversation content and the number of times the rating words appear in the user input conversation content.

[0123] The evaluation parameters can be used to represent the sentence quality of the user's input conversation content and the effectiveness of the user's word training. The evaluation parameters can be either evaluation scores or evaluation levels; no specific limitation is made here.

[0124] For example, with Figure 6 For example, the terminal device can display the evaluation score in the "Score for This Session" section of the dialogue interface. To more accurately evaluate the vocabulary learning effect of the user's input conversation content, the terminal device can determine the total chat score, highest score, number of sentences created, vocabulary size per sentence, total chat vocabulary, and repetition rate of each sentence based on each user's input conversation content and each conversation content, and display these values ​​separately in the dialogue interface. Furthermore, when the evaluation score or total chat score reaches a preset value, the terminal device can also output encouraging voice messages or pop up encouraging prompts in the dialogue interface, such as "You're great," to encourage the user and motivate them to create high-quality sentences.

[0125] In this embodiment, the terminal device determines the complexity of the user-input conversation content based on the number of keywords and grammatical structure in the user-input conversation content, and obtains the number of times the scoring words appear in the user-input conversation content. Based on the complexity of the user-input conversation content and the number of times the scoring words appear in the user-input conversation content, the device determines the evaluation parameters corresponding to the user-input conversation content, and intuitively evaluates the quality of the user's sentence construction and the effect of word learning. This can more effectively encourage users to construct high-quality sentences and improve the efficiency of word training.

[0126] Optionally, based on the above embodiments, such as Figure 10 As shown, the above-mentioned method for generating conversational content for language and vocabulary learning training also includes:

[0127] S601. After obtaining the user's input conversation content, the terminal device increases the number of recorded sentence constructions by a preset value.

[0128] Specifically, the terminal device can record the user's current cumulative number of sentence creations. After obtaining the user's input conversation content, the cumulative number of sentence creations is increased by a preset value, for example, the preset value can be 1.

[0129] S602. When the number of sentence creations after adding a preset value equals the preset number, the terminal device responds to the user's end-of-session operation by outputting a prompt message, which prompts the user to share the session.

[0130] The prompts can be visual prompts or audio prompts.

[0131] It is understandable that when the number of sentences created after increasing the preset value equals the preset number, it means that the number of sentences created by the user in this round of word learning has met the minimum number required to complete this round of conversation.

[0132] Specifically, if the number of sentence creation attempts after adding a preset value equals the preset number, the terminal device can respond to the user's end-of-session operation by outputting a prompt message, prompting the user to share the session.

[0133] For example, taking a prompt message as an example, the dialog interface may display an "End Dialogue" button. If the number of sentence creation attempts after adding a preset value equals the preset number, the terminal device, in response to the user clicking the "End Dialogue" button, displays the prompt message. The prompt message may include a share button and a cancel button to support user sharing of the conversation. The conversation may include user-input conversation content and the conversation content itself. Furthermore, the computer device may return to the dialog interface in response to the user clicking the cancel button.

[0134] S603. The terminal device responds to the user's confirmation of the prompt information and transmits the session to the sharing platform.

[0135] The sharing platform can be a public information sharing platform such as a social media platform or a chat software platform.

[0136] Specifically, the terminal device can transmit the session to the sharing platform in response to the user's confirmation of the prompt. Furthermore, the computer device can return to the dialog interface in response to the user's cancellation of the prompt.

[0137] For example, such as Figure 11 As shown, when a user completes a preset number of sentence-building exercises and clicks the "End Dialogue" button, the terminal device receives the click and displays "Dialogue Ended" in response. A prompt box appears in the dialogue interface, including the text "Would you like to publish this conversation to a sharing platform?", a "Yes" share button, and a "No" cancel button. When the user clicks the "Yes" button, the terminal device responds by transmitting the conversation from the interface to the sharing platform.

[0138] Understandably, if the number of sentence-making attempts after increasing the preset value is less than the preset number, the "End Dialogue" button can be locked and cannot be operated, thus indicating that the current round of vocabulary learning has not yet ended.

[0139] In this embodiment, after the terminal device obtains the user's input conversation content, it increases the recorded number of sentence constructions by a preset value. When the number of sentence constructions after increasing the preset value equals the preset number, it responds to the user's end conversation operation by outputting a prompt message to prompt the user to share the conversation. In response to the user's confirmation of the prompt message, it transmits the conversation to the sharing platform, thereby allowing the user to publicly share the current round of word learning process with others on the sharing platform, enhancing the interactivity of word learning and thus improving the efficiency of word learning.

[0140] Optionally, based on the above embodiments, the conversation content includes conversational sentences and conversational voice data, such as... Figure 12 As shown, the above-mentioned method for generating conversational content for language and vocabulary learning training also includes:

[0141] S701, The terminal device outputs conversational voice data, which is used to guide the user's voice training.

[0142] Specifically, after determining a conversation sentence, the terminal device can perform speech conversion on the conversation sentence to obtain conversation speech data. Alternatively, after determining a conversation sentence, the terminal device can determine the conversation speech data corresponding to the conversation sentence from a preset conversation speech database and obtain that conversation speech data.

[0143] S702. After the terminal device outputs the conversational voice data, it displays the conversational sentences.

[0144] Specifically, the terminal device can display conversational sentences after outputting conversational voice data.

[0145] For example, with Figure 6 For example, the dialogue interface also displays a "Listening Training Mode" button. When the user checks the box before the Listening Training Mode, the terminal device can respond to the user's operation of the "Listening Training Mode" button, and after outputting the dialogue voice data, display the dialogue sentence.

[0146] In this embodiment, the terminal device outputs conversational speech data to guide the user's speech training, and displays conversational sentences after outputting the conversational speech data, thereby helping the user master the usage of at least one target word while also training the user's listening skills.

[0147] Optionally, based on the above embodiments, the method for generating conversational content for language and vocabulary learning training further includes:

[0148] The terminal device responds to the user's translation operation on the conversation by converting the user's input conversation content into first data in a preset language type and converting the conversation content into second data in a preset language type, and displays the first data and the second data in the dialogue interface.

[0149] The preset language type can be set according to user needs, such as Chinese.

[0150] Specifically, the dialogue interface also displays a translation button. During the word learning process, the terminal device can respond to the user's operation of the translation button, translating the user-input dialogue content into first data in a preset language type and second data in a preset language type, and displaying the first and second data in the relevant position in the dialogue interface. For example, the translated first or second data is displayed below each dialogue in the dialogue interface. The first or second data can be text data.

[0151] For example, such as Figure 13 As shown, the terminal device can respond to the user's operation of the "machine translation" button, displaying the corresponding Chinese translation below the user's input dialogue content in the dialog interface.

[0152] In this embodiment, the terminal device responds to the user's operation of the translation button by converting the user's input conversation content into first data of a preset language type and converting the conversation content into second data of a preset language type. The first data and second data are then displayed in the dialogue interface, thereby providing a more intuitive and vivid presentation of the dialogue process. This helps the user to correct errors based on the first data and second data, thus improving the efficiency of word learning.

[0153] The foregoing primarily describes the solutions provided by the embodiments of the present invention from the perspective of terminal devices. It is understood that, in order to achieve the above functions, the device includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the algorithm steps of the examples described in the embodiments disclosed herein, the present invention can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0154] Figure 14 A schematic diagram of the composition of a possible conversational content generation device for language and vocabulary learning training is shown, such as... Figure 14 As shown, the device for generating conversational content for language and vocabulary learning training may include an acquisition module 1501 and a generation module 1502.

[0155] The acquisition module 1501 is used to acquire at least one target word, which is used for user sentence construction training; in response to user input operations, it acquires user input conversation content.

[0156] The generation module 1502 is used to generate conversation content based on user input and at least one target word; the conversation content is used to guide the user to use at least one target word in the conversation.

[0157] Optionally, the user input conversation content includes at least one of the following: input sentence, input voice data, where the input voice data corresponds to the input sentence; the conversation content includes at least one of the following: conversation sentence, conversation voice data, conversation image, conversation video; wherein, the conversation voice data is data obtained after converting the conversation sentence into speech, the conversation image contains the conversation sentence, and the conversation video is a video generated based on the conversation sentence.

[0158] Optionally, the generation module 1502 is specifically used to: determine M candidate conversation contents that match the user input conversation content, where M is a positive integer; when M equals 1, determine the candidate conversation contents as conversation content; when M is greater than 1, determine related words based on at least one target word, and determine the degree of relevance between the related words and each candidate conversation content, and determine the candidate conversation content corresponding to the maximum degree of relevance among the M relevance values ​​as conversation content; wherein, the related words include words that are semantically the same or similar to words in at least one target word, and the degree of relevance is used to indicate the semantic similarity between the related words and each candidate conversation content.

[0159] Optionally, the generation module 1502 is specifically used to: obtain keywords in each candidate session content based on the part of speech of the words included in each candidate session content; determine the relevance of each related word to each candidate session content based on the relevance of each related word to each keyword included in each candidate session content; and determine the relevance of each related word to each candidate session content based on the relevance of each related word to each candidate session content.

[0160] Optionally, the generation module 1502 is specifically used to: determine the similarity between the user-input conversation content and each sentence in the preset sentence library; determine the sentences in the preset sentence library with a similarity greater than a preset threshold as candidate conversation content, and obtain M candidate conversation content.

[0161] Optional, such as Figure 15As shown, the device for generating conversational content for language and vocabulary learning training also includes an evaluation module 1503. The evaluation module 1503 is used to: determine the complexity of the user-input conversational content based on the number of keywords and grammatical structure in the user-input conversational content. The acquisition module 1501 is further used to acquire the number of times a scoring word appears in the user-input conversational content, where the scoring word is a word from at least one target word. The evaluation module 1503 is also used to determine the evaluation parameters corresponding to the user-input conversational content based on the complexity of the user-input conversational content and the number of times the scoring word appears in the user-input conversational content.

[0162] In conjunction with the second aspect, such as Figure 16 As shown, in one possible implementation, the device for generating conversational content for language and vocabulary learning training further includes an increment module 1504, an output module 1505, and a sharing module 1506. The increment module 1504 is used to: after acquiring user-inputted conversational content, increment the recorded number of sentence constructions by a preset value. The output module 1505 is used to, in response to the user's end-of-conversation operation, output a prompt message when the number of sentence constructions after incrementing the preset value equals the preset number, prompting the user to share the conversation. The sharing module 1506 is used to, in response to the user's confirmation of the prompt message, transmit the conversation to a sharing platform.

[0163] Optionally, the conversation content includes conversation sentences and conversation voice data. The output module 1505 is also used to: output conversation voice data, which is used to guide the user's voice training; and after outputting the conversation voice data, display the conversation sentences.

[0164] Optionally, the device for generating conversational content for language and vocabulary learning training also includes a translation module, which is used to: in response to a user's translation operation on the conversation, convert the user-input conversational content into first data of a preset language type, convert the conversational content into second data of a preset language type, and display the first data and the second data in the dialogue interface.

[0165] Optionally, the acquisition module 1501 is specifically used for: acquiring at least one target word in response to a user's input of a word in the dialogue interface; or, acquiring target speech data in response to a user's voice input in the dialogue interface, and acquiring at least one target word based on the target speech data; or, displaying a word library button in the dialogue interface, and displaying a word list in response to a user's operation on the word library button, the word list including words from a preset word library; acquiring at least one target word in response to a user's selection operation in the word list; or, acquiring word learning data in response to a word training instruction, and using words from a preset time period in the word learning data as at least one target word.

[0166] The conversation content generation apparatus for language and vocabulary learning training provided in this embodiment of the invention is used to execute the conversation content generation method for language and vocabulary learning training described above, and therefore can achieve the same effect as the conversation content generation method for language and vocabulary learning training described above.

[0167] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions within the technical scope disclosed in the present invention should be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for generating conversational content for language and vocabulary learning training, characterized in that, include: Acquire at least one target word, which is used for user sentence construction training; Responding to user input, obtain the content of the user input session; Generate conversation content based on the user-input conversation content and the at least one target word; The conversation content is used to guide the user to use at least one of the target words in the conversation; The process of generating conversation content based on the user-input conversation content and the at least one target word includes: Determine M candidate session contents that match the user input session content, where M is a positive integer; When M equals 1, the alternative session content is determined as the session content; When M is greater than 1, related words are determined based on the at least one target word, and the degree of relevance between the related words and each candidate conversation content is determined. The candidate conversation content corresponding to the maximum degree of relevance among the M relevance values ​​is determined as the conversation content. The associated words include words that are semantically identical or similar to words in the at least one target word, and the association degree is used to indicate the semantic similarity between the associated words and each candidate session content.

2. The method for generating conversational content for language and vocabulary learning training according to claim 1, characterized in that, The user input conversation content includes at least one of the following: input sentence, input voice data, wherein the input voice data corresponds to the input sentence; The conversation content includes at least one of the following: conversation sentences, conversation voice data, conversation images, and conversation videos; The conversational voice data is data obtained by converting the conversational sentences into speech; the conversational image includes the conversational sentences in its image content; and the conversational video is a video generated based on the conversational sentences.

3. The method for generating conversational content for language and vocabulary learning training according to claim 1, characterized in that, Determining the relevance of the associated terms to each candidate conversation content includes: Based on the part of speech of the words included in each candidate conversation, obtain the keywords in each candidate conversation content; The relevance of each related word to each candidate conversation content is determined based on the relevance of each related word to each keyword included in each candidate conversation content. The degree of relevance between each associated word and each candidate conversation content is determined based on the degree of relevance between each associated word and each candidate conversation content.

4. The method for generating conversational content for language and vocabulary learning training according to claim 1, characterized in that, The determination of M candidate session contents that match the user input session content includes: Determine the similarity between the user-input conversation content and each sentence in a preset sentence database; Sentences with a similarity greater than a preset threshold in the preset sentence library are identified as candidate conversation content, thus obtaining the M candidate conversation contents.

5. The method for generating conversational content for language and vocabulary learning training according to claim 1 or 2, characterized in that, The method for generating conversational content for language and vocabulary learning training further includes: The complexity of the user-input conversation content is determined based on the number of keywords and the grammatical structure in the user-input conversation content. The number of times a rating word appears in the user-input conversation content is obtained, wherein the rating word is a word from the at least one target word; The evaluation parameters corresponding to the user input conversation content are determined based on the complexity of the user input conversation content and the number of times the rating words appear in the user input conversation content.

6. The method for generating conversational content for language and vocabulary learning training according to claim 1 or 2, characterized in that, The method for generating conversational content for language and vocabulary learning training further includes: After obtaining the user's input conversation content, the number of recorded sentence constructions will be increased by a preset value; If the number of sentence creations after increasing the preset value equals the preset number, in response to the user's end-of-session operation, a prompt message is output, which prompts the user to share the session. In response to the user's confirmation of the prompt information, the session is transmitted to the sharing platform.

7. An apparatus for generating conversational content for language and vocabulary learning training, characterized in that, include: The acquisition module is used to acquire at least one target word, which is used for user sentence construction training. Responding to user input, obtain the content of the user input session; A generation module is configured to generate conversation content based on the user-input conversation content and the at least one target word; the conversation content is used to guide the user to use the at least one target word in the conversation; The generation module is specifically used to: determine M candidate session contents that match the user input session content, where M is a positive integer; When M equals 1, the alternative session content is determined as the session content; When M is greater than 1, associated words are determined based on the at least one target word, and the degree of association between the associated words and each candidate session content is determined. The candidate session content corresponding to the maximum degree of association among the M degrees of association is determined as the session content. The associated words include words that are semantically the same as or similar to words in the at least one target word, and the degree of association is used to indicate the semantic similarity between the associated words and each candidate session content.

8. The apparatus for generating conversational content for language and vocabulary learning training according to claim 7, characterized in that, The user input conversation content includes at least one of the following: input sentence, input voice data, wherein the input voice data corresponds to the input sentence; The conversation content includes at least one of the following: conversation sentences, conversation voice data, conversation images, and conversation videos; The conversational voice data is data obtained by converting the conversational sentences into speech; the conversational image includes the conversational sentences in its image content; and the conversational video is a video generated based on the conversational sentences.

9. The apparatus for generating conversational content for language and vocabulary learning training according to claim 7, characterized in that, The generation module is specifically used for: Based on the part of speech of the words included in each candidate conversation, obtain the keywords in each candidate conversation content; The relevance of each related word to each candidate conversation content is determined based on the relevance of each related word to each keyword included in each candidate conversation content. The degree of relevance between each associated word and each candidate conversation content is determined based on the degree of relevance between each associated word and each candidate conversation content.

10. The apparatus for generating conversational content for language and vocabulary learning training according to claim 7, characterized in that, The generation module is specifically used for: Determine the similarity between the user-input conversation content and each sentence in a preset sentence database; Sentences with a similarity greater than a preset threshold in the preset sentence library are identified as candidate conversation content, thus obtaining the M candidate conversation contents.

11. The apparatus for generating conversational content for language and vocabulary learning training according to claim 7 or 8, characterized in that, The device for generating conversational content for language and vocabulary learning training also includes an evaluation module; The evaluation module is used to determine the complexity of the user-input conversation content based on the number of keywords and the grammatical structure in the user-input conversation content. The acquisition module is further configured to acquire the number of times the rating words appear in the user input conversation content, wherein the rating words are words in the at least one target word; The evaluation module is also used to determine the evaluation parameters corresponding to the user input conversation content based on the complexity of the user input conversation content and the number of times the rating words appear in the user input conversation content.

12. The apparatus for generating conversational content for language and vocabulary learning training according to claim 7 or 8, characterized in that, The device for generating conversational content for language and vocabulary learning training also includes an adding module, an output module, and a sharing module. The addition module is used to increase the recorded number of sentence constructions by a preset value after obtaining the user input conversation content; The output module is used to output a prompt message in response to the user's end-of-session operation when the number of sentence creations after the addition module adds the preset value equals the preset number of sentences. The prompt message is used to prompt the user to share the session. The sharing module is used to transmit the session to the sharing platform in response to the user's confirmation operation of the prompt information output by the output module.

13. A computer device, characterized in that, The computer device includes a processor and a memory; the memory is used to store computer program code, the computer program code including computer instructions; when the processor executes the computer instructions, the computer device performs the method for generating conversational content for language and vocabulary learning training as described in any one of claims 1-6.

14. A computer-readable storage medium, characterized in that, Includes computer instructions that, when executed on a computer device, cause the computer device to perform the method for generating conversational content for language and vocabulary learning training as described in any one of claims 1-6.

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