Data processing method and device based on cross-language chat, equipment and medium

Abstract

The invention discloses a data processing method and device based on cross-language chat, equipment and a medium, and belongs to the field of data processing. The method comprises the steps that session log information of a chat session where a first user is located is acquired, the session log information comprises session participating users, chat session original texts, chat session translation texts and language information in the chat session, and the language of the first user is different from that of a second user; on the basis of the session log information, cross-language chat portrait information of a chat session of the first user is obtained, the cross-language chat portrait information is associated with a session text set corresponding to a language of a second user, and the session text set comprises a chat session original text and / or a chat session translation text; and in response to a target operation for indicating the target language for the cross-language chat portrait information, displaying a session text set corresponding to the target language. According to the embodiment of the invention, the language ability of the user can be improved.

Description

Technical Field

[0001] This application belongs to the field of data processing, and particularly relates to a data processing method, apparatus, device and medium based on cross-language chatting. Background Art

[0002] With the continuous development of information technology, the use of instant messaging applications has become increasingly common. Through instant messaging applications, users can chat individually with each other, or multiple users can form a communication group for group chatting. The two or more chat participants involved in the chat of instant messaging applications may be users who speak different languages. For example, in a one-on-one chat, user A1 is a Chinese user and user A2 is a Thai user. Cross-language chatting may occur between users who speak different languages. If different language users want to communicate smoothly, they need to use a third language. However, there is also a situation where users are not proficient in the third language, resulting in poor communication effects and prone to communication errors. When users who speak different languages use instant messaging applications, they can also manually trigger the translation of the message they want to send into the language of the other user, so as to provide the other user with a message in the same language as the other user. Similarly, users also need to manually translate the message sent by the other user into their own language for easy understanding. However, the above processing method can only solve the surface problem and does not help much with the user's own language ability, and it is difficult to provide assistance to users to improve their language ability. Summary of the Invention

[0003] The embodiments of this application provide a data processing method, apparatus, device and medium based on cross-language chatting, which can provide assistance to improve the user's language ability and better help users communicate with users of other languages.

[0004] In a first aspect, the embodiments of this application provide a data processing method based on cross-language chatting, including: obtaining session log information of a chat session where a first user is located, the session log information including session participating users in the chat session, the original chat session text of the session participating users, the translated chat session text of the session participating users, and the language information of the session participating users, the session participating users including the first user and a second user, the language of the first user being different from the language of the second user, and the chat session of the first user including the original chat session text and the translated chat session text that are consistent with the language of the first user; based on the session log information, obtaining cross-language chat portrait information of the chat session of the first user, the cross-language chat portrait information being associated with a session text set corresponding to the language of the second user, the session text set including the original chat session text and / or the translated chat session text; in response to a target operation indicating a target language for the cross-language chat portrait information, displaying the session text set corresponding to the target language.

[0005] In a second aspect, an embodiment of the present application provides a data processing device based on cross-language chatting, including: an information acquisition module, configured to acquire session log information of a chat session where a first user is located, the session log information including session participating users in the chat session, the original chat text of the session participating users, the translated chat text of the session participating users, and the language information of the session participating users, the session participating users including the first user and the second user, the language of the first user being different from that of the second user, and the chat session of the first user including the original chat text and the translated chat text that are consistent with the language of the first user; a portrait generation module, configured to obtain cross-language chatting portrait information of the chat session of the first user based on the session log information, the cross-language chatting portrait information being associated with a session text set corresponding to the language of the second user, the session text set including the original chat text and / or the translated chat text; and an operation execution module, configured to display the session text set corresponding to the target language in response to a target operation for the cross-language chatting portrait information that indicates a target language.

[0006] In a third aspect, an embodiment of the present application provides a data processing device based on cross-language chatting, including: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the data processing method based on cross-language chatting in the first aspect is implemented.

[0007] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the data processing method based on cross-language chatting in the first aspect is implemented.

[0008] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, the data processing method based on cross-language chatting in the first aspect is implemented.

[0009] An embodiment of the present application provides a data processing method, device, equipment, and medium based on cross-language chatting. The cross-language chatting portrait information of the chat session of the first user can be obtained according to the session log information of the cross-language chat session between the first user and the second user. The cross-language chatting portrait information can depict the usage situation of languages other than the first user's own language involved in the chat session of the first user. The cross-language chatting portrait information is associated with a session text set corresponding to the language of the second user. When the user performs an operation on the cross-language chatting portrait information for a certain language, the session text set corresponding to the language is displayed to the first user, so as to help the first user learn and train cross-language chatting based on the actual chat session, provide assistance for the first user to learn a language other than their own language to improve the first user's language ability, and better help the user communicate with users of other languages. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] To more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required for the embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0011] Figure 1 It is a flowchart of a data processing method based on cross-lingual chat provided by an embodiment of the present application;

[0012] Figure 2 It is a schematic diagram of an example of cross-lingual chat portrait information provided by an embodiment of the present application;

[0013] Figure 3 It is a schematic diagram of an example of a vocabulary set corresponding to English within a preset time period provided by an embodiment of the present application;

[0014] Figure 4 It is a schematic diagram of an example of a session text set provided by an embodiment of the present application;

[0015] Figure 5 It is a schematic diagram of an example of the link relationship among cross-lingual chat portrait information, vocabulary set, and session text set provided by an embodiment of the present application;

[0016] Figure 6 It is a schematic diagram of an example of the system architecture of a data processing method based on cross-lingual chat provided by an embodiment of the present application;

[0017] Figure 7 It is a schematic diagram of another example of the system architecture of a data processing method based on cross-lingual chat provided by an embodiment of the present application;

[0018] Figure 8 It is a schematic diagram of the structure of a data processing device based on cross-lingual chat provided by an embodiment of the present application;

[0019] Figure 9 It is a schematic diagram of the structure of a data processing device based on cross-lingual chat provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] Aspects and exemplary embodiments of the present application will be described in detail below. To make the objectives, technical solutions, and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by showing examples of the present application. It should be noted that the acquisition, storage, use, processing, etc. of information and data in the embodiments of the present application have obtained the authorization of users or relevant institutions, and comply with the relevant provisions of national laws and regulations.

[0021] With the continuous development of information technology, the use of instant messaging applications has become increasingly common. Through instant messaging applications, users can chat individually with each other or multiple users can form a communication group for group chat. The two or more chat participants involved in the chat of the instant messaging application may be users using different languages. For example, in a one-on-one chat, user A1 is a Chinese user and user A2 is a Thai user. Cross-language chatting may occur between users of different languages. If different language users want to communicate smoothly, they need to use a third language. However, there may also be situations where users are not proficient in the third language, resulting in poor communication effects and easy communication errors. When users of different languages use instant messaging applications, they can also manually trigger the translation of the messages they want to send into the language of the other user, so as to provide the other user with messages in the same language as the other user. Similarly, users also need to manually translate the messages sent by the other user into their own language for easy understanding. However, the above processing method can only solve the surface problem and does not help much with the user's own language ability, and it is difficult to provide assistance to users to improve their language ability.

[0022] The present application provides a data processing method, device, equipment, and medium based on cross-language chatting, which obtains the session log information of the chat session where the user is located. The session log information records the relevant information of the cross-language chat between the user and other users. The cross-language chat portrait information of the user is characterized according to the session log information. The session text in a language different from the user's language can be automatically pushed to the user through the cross-language chat portrait information, so as to help chat participants learn and train cross-language chatting based on practical chat conversations, provide assistance to users to improve their language ability, better help users communicate with users of other languages, and improve the user experience.

[0023] The data processing method, device, equipment, medium, and program product based on cross-language chatting provided by the present application will be described separately below.

[0024] The present application provides a data processing method based on cross - language chatting, which can be used in scenarios where users using different languages chat through an instant messaging application. The data processing method based on cross - language chatting can be executed by a data processing device or equipment for cross - language chatting. The data processing device or equipment for cross - language chatting can be implemented as a user terminal or a remote server, which is not limited herein. Figure 1 It is a flowchart of the data processing method based on cross - language chatting provided by an embodiment of the present application. As Figure 1 shown, the data processing method based on cross - language chatting may include steps S101 to S103.

[0025] In step S101, obtain the session log information of the chat session where the first user is located.

[0026] The session participating users of the chat session include a first user and a second user. The first user is the local user, and the second user is the peer user having a chat session with the first user. Other users in the chat session except the first user can be regarded as the second user. The chat session where the first user is located may include a one - to - one single - chat session between the first user and the second user, or may also include a one - to - many group - chat session between the first user and the second user. The language of the first user is different from the language of the second user, that is, the chat session between the first user and the second user is a cross - language chat session. During the cross - language chatting between the first user and the second user, for the first user, when receiving the original chat session text sent by the second user, if the language of the original chat session text is different from the language of the first user, the translation function is automatically triggered to translate the original chat session text into a chat session translation text consistent with the language of the first user, and the chat session translation text is displayed to the first user, and the original chat session text in the language of the first user sent by the first user is also displayed to the first user. During the cross - language chatting between the first user and the second user, for the second user, when receiving the original chat session text sent by the first user, if the language of the original chat session text is different from the language of the second user, the translation function is automatically triggered to translate the original chat session text into a chat session translation text consistent with the language of the second user, and the chat session translation text is displayed to the second user, and the original chat session text in the language of the second user sent by the second user is also displayed to the second user. Correspondingly, the chat session of the first user includes the original chat session text consistent with the language of the first user and the chat session translation text. Similarly, the chat session of the second user includes the original chat session text consistent with the language of the second user and the chat session translation text.

[0027] The session log information can be obtained from the user terminal where the instant messaging application is located, or from the remote server of the instant messaging application, or from the data uploaded by the user. There is no limitation on the way to obtain the session log information here. The session log information includes the session participating users in the chat session, the original text of the chat session of the session participating users, the translated text of the chat session of the session participating users, and the language information of the session participating users. The session participating users include the users participating in the chat session. The session participating users include a first user and a second user. For the specific content of the first user and the second user, reference can be made to the relevant descriptions in the foregoing text, which will not be elaborated here. The original text of the chat session of the session participating users includes the original text of the chat session sent by the first user and the original text of the chat session sent by the second user. The original text of the chat session is the original text of the session participating users in the chat session and is untranslated text. The translated text of the chat session of the session participating users includes the translated text of the chat session received by the first user and the translated text of the chat session received by the second user. The language of the translated text of the chat session received by the first user is the same as the language of the first user. The translated text of the chat session received by the first user is the original text of the chat session sent by the second user obtained through automatic translation. The language of the translated text of the chat session received by the second user is the same as the language of the second user. The translated text of the chat session received by the second user is the original text of the chat session sent by the first user obtained through automatic translation. The language information of the session participating users can represent the language used by the session participating users for chatting. The language information of the first user can represent the language used by the first user for chatting. The language information of the second user can represent the language used by the second user for chatting.

[0028] In some examples, the session log information of the chat session where the first user is located can be obtained periodically according to a preset statistical period. The preset statistical period can be set according to scenarios, requirements, experience, etc. For example, the preset statistical period can be 24 hours or one week. After setting the preset statistical period, the session log information of the chat session where the first user is located can be obtained regularly. For example, the session log information of the first user can be obtained regularly at 20:00 every night. The preset statistical period and the time point for obtaining the session log information are adjustable. This adjustment can be automatically adjusted according to the user's behavior habits or can be adjusted in response to the user's input.

[0029] In some other examples, in response to an input operation of a first user, session log information of the chat session where the first user is located during the time period indicated by the input operation is obtained. The time period indicated by the input operation can be customized by the user. For example, if the time period indicated by the input operation is from 00:00 on March 1, 2025 to 00:00 on March 2, 2025, then the session log information from 00:00 on March 1, 2025 to 00:00 on March 2, 2025 is obtained. The first user can trigger the acquisition of the session log information through the input operation when there is a need.

[0030] It should be noted that the languages in the embodiments of the present application can be classified according to one or more of factors such as language systems, language usage regions, language functions, and language structures, and are not limited herein. For example, the languages can include English, Chinese, Thai, Japanese, etc., and can also include dialects such as Cantonese, Hakka, Suzhou dialect, and Shanghai dialect. If one party cannot fully understand the language of the other party on the basis that one party has not learned the language of the other party, it is considered that the two parties use different languages.

[0031] In step S102, based on the session log information, cross-language chat portrait information of the chat session of the first user is obtained.

[0032] Each time the session log information is obtained, the cross-language chat portrait information of the first user can be obtained once according to the session log information obtained this time. For example, according to a preset statistical period, the cross-language chat portrait information of the chat session of the first user can be periodically obtained based on the session log information, that is, the cross-language chat portrait information of the chat session of the first user can be obtained regularly based on the session log information. Another example is that in response to an input operation of the first user, the session log information of the time period indicated by the input operation is obtained, and according to this session log information, the cross-language chat portrait information of the first user in this time period is obtained.

[0033] The cross-language chat portrait information of the first user can characterize the usage of languages other than the first user's own language involved in the chat session of the first user. According to the cross-language chat portrait information of the first user, relevant information about the languages involved in the chat session of the first user can be known. In some examples, the cross-language chat portrait information of the first user may include the number of second users corresponding to the languages other than the first user's own language related to the chat session of the first user and session-related information. The session-related information may include, but is not limited to, information such as session duration, number of sessions, and user identifiers of the second users involved in the session. For example, the cross-language chat portrait information of the first user can be implemented as a chart. Figure 2 Schematic diagram of an example of the cross-language chat portrait information provided by the embodiments of the present application. Figure 2Recorded the cross - language chat portrait information within a preset time period. The cross - language chat portrait information includes language, the number of second users, conversation duration, and conversation times, and is obtained by Figure 2 It can be seen that the languages other than the first user's own language related to the first user's chat conversations include English, Thai, and Lao. The number of second users who chat with the first user in English is 5, the conversation duration involving English is 20 hours, and the number of conversation times involving English is 30 times. The number of second users who chat with the first user in Thai is 2, the conversation duration involving Thai is 15 hours, and the number of conversation times involving Thai is 20 times. The number of second users who chat with the first user in Lao is 4, the conversation duration involving Lao is 10 hours, and the number of conversation times involving Lao is 10 times.

[0034] The cross - language chat portrait information is associated with a set of conversation texts corresponding to the languages of the second users. The set of conversation texts includes the original chat conversation texts and / or the translated chat conversation texts. A first user may have cross - language chats with multiple second users. The languages of the multiple second users may be the same or different, and the sets of conversation texts associated with different languages are different. Each language in the cross - language chat portrait information can be associated with one or more sets of conversation texts, but the languages of the original chat conversation texts and the translated chat conversation texts in the set of conversation texts associated with one language are both this one language. The association between the cross - language chat portrait information and the set of conversation texts corresponding to the languages of the second users can be achieved through links, and the links can be single - layer links or multi - layer links.

[0035] After obtaining the cross - language chat portrait information of the first user, the cross - language chat portrait information of the first user can be displayed to the first user. For example, the cross - language chat portrait information of the first user can be pushed to the client of the first user's user terminal through the human - machine conversation window.

[0036] In step S103, in response to a target operation indicating the target language for the cross - language chat portrait information, display the set of conversation texts corresponding to the target language.

[0037] The user can operate on the cross - language chat portrait information to select the target language, and the target operation includes the operation of selecting the target language. Through the target operation, the target language selected by the user can be determined. Based on the association relationship between the language and the set of conversation texts, the set of conversation texts corresponding to the target language is obtained and displayed to the first user. For example, the set of conversation texts corresponding to the target language can be pushed to the client of the first user's user terminal through the human - machine conversation window. The user can learn a non - native language according to the set of conversation texts corresponding to the non - native language selected, and the set of conversation texts comes from the first user's chat conversations, which is more in line with the actual scenario of the first user's chat and can effectively assist the first user in learning a non - native language.

[0038] In the embodiments of the present application, the cross - language chat portrait information of the first user's chat session can be obtained according to the session log information of the cross - language chat session between the first user and the second user. The cross - language chat portrait information can depict the usage of languages other than the first user's own language involved in the first user's chat session. The cross - language chat portrait information is associated with a set of session texts corresponding to the language of the second user. When the user operates on the cross - language chat portrait information for a certain language, the set of session texts corresponding to that language is displayed to the first user, so as to help the first user learn and train cross - language chat based on the actual chat session, provide assistance for the first user to learn a language other than their own to improve the first user's language ability, better help the user communicate with users of other languages, and improve the user experience.

[0039] In some embodiments, the cross - language chat portrait information of the first user includes the number of second users corresponding to the languages involved in the chat session and session - related information. The number of second users corresponding to each language and session - related information can be counted according to the language information of the session - participating users in the session log information; the cross - language chat portrait information is obtained according to each language and the number of second users corresponding to each language and session - related information. Each chat message in the chat session can be parsed to obtain the language of each chat message, the session - participating users, the original chat session text, and the translated chat session text, classified according to the language, and the number of second users, session duration, session times, etc. corresponding to each language are counted. The number of second users corresponding to the language and session - related information are structured to form the cross - language chat portrait information.

[0040] In some examples, the languages, as well as the number of second users corresponding to each language and session-related information, may be sorted according to the number of second users corresponding to each language and / or session-related information; according to the sorted languages, as well as the number of second users corresponding to each language and session-related information, cross-language chat portrait information is obtained. The sorting may be performed according to the number of second users. For example, the number of second users corresponding to each language and session-related information may be sorted in descending order of the number of second users. The sorting may be performed according to session-related information. For example, the number of second users corresponding to each language and session-related information may be sorted in descending order of session duration, or the number of second users corresponding to each language and session-related information may be sorted in descending order of the number of sessions. The sorting may also be performed by combining the number of second users and session-related information. For example, the sorting may be first performed independently according to the number of second users, session duration, and number of sessions respectively, and then, according to the sorting corresponding to the number of second users, the sorting corresponding to session duration, and the sorting corresponding to the number of sessions, the final sorting of the number of second users corresponding to each language and session-related information is obtained comprehensively. For example, the cross-language chat portrait information is as Figure 2 shown Figure 2 The cross-language chat portrait information in is determined according to the sorting in descending order of session duration and the sorting in descending order of the number of sessions. The English language, which has the highest session duration and the most sessions, is ranked first in the cross-language chat portrait information. The Thai language, which has the second highest session duration and the second most sessions, is ranked second in the cross-language chat portrait information. The Lao language, which has the third highest session duration and the third most sessions, is ranked third in the cross-language chat portrait information.

[0041] Through sorting, the contact frequency of non-native languages involved by the first user can be obtained. Sorting according to this contact frequency can facilitate the first user to quickly find the more frequently used languages among non-native languages for learning.

[0042] In some embodiments, the cross-lingual chat portrait information is linked to a vocabulary set corresponding to the language of the second user. Different languages correspond to different vocabulary sets, and one language can be linked to one vocabulary set. The vocabulary set includes at least one vocabulary, and the vocabularies in the vocabulary set are extracted from the chat conversations between the first user and the second user. In some examples, the original chat conversation text and the translated chat conversation text corresponding to the same language in the conversation log information can be parsed to obtain the keywords in the original chat conversation text and the keywords in the translated chat conversation text; the keywords in the original chat conversation text and the keywords in the translated chat conversation text are aggregated to form a vocabulary set corresponding to each language. The keywords can be obtained based on one or more of the following methods: word frequency statistics, term frequency-inverse document frequency (TF-IDF) statistics, word vector clustering, graph ranking extraction, deep learning extraction. For example, using the word frequency statistics method, the vocabularies with a frequency higher than a preset frequency threshold in the original chat conversation text and the translated chat conversation text can be determined as keywords and placed in the vocabulary set; using the term frequency-inverse document frequency statistics method, the vocabularies with a term frequency-inverse document frequency higher than a preset frequency statistics threshold in the original chat conversation text and the translated chat conversation text can be determined as keywords and placed in the vocabulary set; using the word vector clustering method, the vocabularies in the original chat conversation text and the translated chat conversation text can be converted into word vectors, and clustering is performed based on the word vectors to extract representative vocabularies as keywords and place the keywords in the vocabulary set; using the graph ranking extraction method, a co-occurrence graph of words can be constructed to extract keywords and place the keywords in the vocabulary set; using the deep learning extraction method, the semantic understanding ability of deep learning can be used to extract keywords and place the keywords in the vocabulary set.

[0043] The vocabulary set may further include vocabulary statistical information, which may include, but is not limited to, information such as the number of times a vocabulary appears and the usage of the vocabulary, etc., and is not limited herein. Function controls may also be set in the vocabulary set, and the function controls may include a vocabulary hiding control and a text viewing control. The vocabulary hiding control can be used to hide the corresponding vocabulary. The user can perform a hiding operation on any vocabulary in the vocabulary set. In response to the first user's hiding operation on any vocabulary in the vocabulary set, the vocabulary indicated by the hiding operation can be hidden in the vocabulary set, and the vocabulary indicated by the hiding operation can be stored in the hidden vocabulary set. For example, if the first user is already familiar with a certain or certain vocabularies in the vocabulary set and no longer needs to continue learning that vocabulary, the familiar vocabulary can be hidden and no longer displayed in the vocabulary set, so that the first user can query the vocabulary they need to learn faster in the vocabulary set, avoid the influence of the familiar vocabulary on the display of the vocabulary to be learned, reduce the number of vocabularies that the first user needs to browse in the vocabulary set, and improve the speed at which the first user queries the vocabulary to be learned in the vocabulary set. Each vocabulary in the vocabulary set is linked to a set of conversation texts corresponding to the vocabulary, and different vocabularies are linked to different sets of conversation texts. The set of conversation texts includes the original chat conversation text containing the vocabulary and / or the translated chat conversation text containing the vocabulary. The set of conversation texts also includes one or more of the following: text type, the conversation participants corresponding to the text, and the text sending time. The text type includes the original text and the translated text; the original text indicates that the text is the original text sent by the user, and the text with the text type of the original text is the original chat conversation text; the translated text indicates that the text is the text after the original text sent by the user is translated, and the text with the text type of the translated text is the translated chat conversation text. The conversation participants corresponding to the text are the users who sent the text, and the users who sent the text may include the first user and the second user. The text sending time is the time when the conversation participant sent the text. The text viewing control can be used to jump to the set of conversation texts corresponding to the vocabulary. The vocabulary set can be implemented as a vocabulary list, and the function control can be implemented as a function button. For example, Figure 3 is a schematic diagram of an example of the vocabulary set corresponding to English within a preset time period provided by an embodiment of the present application, Figure 4 is a schematic diagram of an example of the set of conversation texts provided by an embodiment of the present application; as Figure 3 shown, the vocabulary set includes vocabulary, the number of times the vocabulary appears, and function buttons. The vocabulary may include "How", "binlog", "check", and "snapshot". The number of times "How" appears is 15 times, the number of times "binlog" appears is 10 times, the number of times "check" appears is 8 times, and the number of times "snapshot" appears is 5 times. Among the function buttons, there are a vocabulary hiding button and a text viewing button. The vocabulary hiding button can be implemented as Figure 3 "Hide" in Figure 3"View Original Text" in it. If the user wants to hide the word "How" in the vocabulary set, the user can click the "Hide" button corresponding to the word "How" to hide the word "How" in the vocabulary set. If the user wants to learn the original text of the chat session including the word "binlog" and the translated text of the chat session, the user can click the "View Original Text" button corresponding to the word "binlog", and then jump to the session text set linked to the word "binlog" as Figure 4 shown. As Figure 4 shown, the session text set includes the texts "How to check binlog?", "What is binlog?", "Binlog is a databaselog."; the text type of "How to check binlog?" is the original text, the corresponding session participating user is Author, and the text sending time is mm-dd hh:mm:ss; the text type of "What is binlog?" is the translated text, the corresponding session participating user is Author, and the text sending time is mm-dd hh:mm:ss; the text type of "Binlog is a database log." is the translated text, the corresponding session participating user is Me, and the text sending time is mm-dd hh:mm:ss.

[0044] The target operations include a first operation and a second operation. The first operation is an operation indicating the target language, and the first operation can specifically be an operation of selecting the target language. For example, if the cross-language chat portrait information is as Figure 2 shown, the first operation can be an operation of clicking on a specific language. The second operation is an operation indicating the target word, and the second operation can specifically be an operation of selecting a specific word. For example, if the vocabulary set is as Figure 3 shown, the second operation can be an operation of clicking the "View Original Text" button corresponding to the corresponding word. In response to the first operation indicating the target language, the vocabulary set corresponding to the target language can be displayed; in response to the second operation indicating the target word, the session text set corresponding to the target word can be displayed. When the first user performs the first operation on the target language, the user can jump to the vocabulary set corresponding to the target language. Then, when the first user performs the second operation on the target word in the vocabulary set corresponding to the target language, the user can jump to the session text set corresponding to the target word, and the session text set is displayed to the first user, providing the user with text in a language other than the first user's own language for learning. For example, Figure 5 is a schematic diagram showing an example of the link relationship among the cross-language chat portrait information, the vocabulary set, and the session text set provided by an embodiment of the present application. Figure 5 The cross-language chat portrait information in Figure 2 is the same as the cross-language chat portrait information shown Figure 5 The vocabulary set inFigure 3 The vocabulary sets shown are the same, Figure 5 the set of conversation texts in Figure 4 is the same as the set of conversation texts shown. As Figure 5 shown, English in the languages of the cross-lingual chat portrait information is linked to the vocabulary set containing "How", "binlog", "check", and "snapshot". The "View Original" link corresponding to "binlog" in this vocabulary set has a set of conversation texts containing "How to check binlog?", "What is binlog?", and "Binlog is a database log.", Figure 5 The dashed arrows in

[0045] To facilitate understanding, the system architecture of the data processing method based on cross-lingual chat will be described below. Figure 6 is a schematic diagram of an example of the system architecture of the data processing method based on cross-lingual chat provided by the embodiments of this application. As Figure 6 shown, the system architecture 20 may include a text automatic translation unit 21, a conversation log information unit 22, a chat portrait generation unit 23, an auxiliary information promotion unit 24, a vocabulary set management unit 25, and a conversation text set management unit 26.

[0046] The text automatic translation unit 21 can automatically translate the original chat session text in the language of the second user sent by the second user into the translated chat session text in the language of the first user, and can also automatically translate the original chat session text in the language of the first user sent by the first user into the translated chat session text in the language of the second user, where the language of the first user is different from that of the second user. The language of the original chat session text sent by the second user and the language of the translated chat session text sent by the first user are both not the language of the first user himself. The original chat session text, the translated chat session text, the session participating users, and the language information sent by the first user and the second user can be stored as session log information in the session log information unit 22. The session log information unit 22 can provide the session log information to the chat portrait generation unit 23, and the chat portrait generation unit 23 can obtain the cross-language chat portrait information 231 of the first user based on the session log information. The chat portrait generation unit 23 can generate the cross-language chat portrait information 231 regularly. The chat portrait generation unit 23 is communicatively connected to the vocabulary set management unit 25. The vocabulary set management unit 25 can include a vocabulary set 251 and a hidden vocabulary set 252. The cross-language chat portrait information 231 in the chat portrait generation unit 23 can be linked to the vocabulary set 251 in the vocabulary set management unit 25. The vocabulary set management unit 25 is communicatively connected to the session text set management unit 26, and the vocabulary set 251 in the vocabulary set management unit 25 is linked to the session text set 261 in the session text set management unit 26. The session log information unit 22 can provide the original chat session text and the translated chat session text in the session log information to the text set management unit 26 so that the text set management unit 26 can generate a session text set. The auxiliary information promotion unit 24 can push the cross-language chat portrait information 231 to the user terminal 31 of the first user. The user terminal 31 and the system architecture 20 can be independent of each other, or the system architecture 20 can also be integrated in the user terminal 31, which is not limited here.

[0047] Figure 7 It is a schematic diagram of another example of the system architecture of the cross-language chat-based data processing method provided by the embodiment of the present application. Figure 7 The structures of the various parts within the shown system architecture are the same as Figure 6 the structures of the various parts shown, and reference can be made to the relevant content in the above text, which will not be elaborated here. Figure 7 Different from Figure 6 this is that the first user can manually query the cross-language chat portrait information 231 from the chat portrait generation unit 23 through the user terminal 31. Specifically, the first user can actively initiate a cross-language chat portrait query request for a specified period in the human-computer conversation window of the user terminal 31, driving the chat portrait generation unit 23 to generate the cross-language chat portrait information 231 and driving the vocabulary set management unit 25 to generate the vocabulary set 251.

[0048] The present application further provides a data processing device based on cross - language chatting. The data processing device based on cross - language chatting can be implemented as a user terminal or a server, which is not limited herein. Figure 8 FIG. is a schematic structural diagram of a data processing device based on cross - language chatting provided in an embodiment of the present application. As Figure 8 shown, the data processing device 400 based on cross - language chatting may include an information acquisition module 401, a portrait generation module 402, and an operation execution module 403.

[0049] The information acquisition module 401 can be used to acquire the session log information of the chat session where the first user is located.

[0050] The session log information includes the session participating users in the chat session, the original chat text of the session participating users, the translated chat text of the session participating users, and the language information of the session participating users. The session participating users include the first user and the second user. The language of the first user is different from that of the second user. The chat session of the first user includes the original chat text and the translated chat text that are consistent with the language of the first user.

[0051] The portrait generation module 402 can be used to obtain the cross - language chat portrait information of the chat session of the first user based on the session log information.

[0052] The cross - language chat portrait information is associated with a set of session texts corresponding to the language of the second user. The set of session texts includes the original chat text and / or the translated chat text.

[0053] The operation execution module 403 can be used to display the set of session texts corresponding to the target language in response to a target operation indicating the target language for the cross - language chat portrait information.

[0054] In some embodiments, the portrait generation module 402 can be used to: count the number of second users corresponding to each language and session - related information according to the language information of the session participating users in the session log information; obtain the cross - language chat portrait information according to each language and the number of second users corresponding to each language and the session - related information.

[0055] In some examples, the portrait generation module 402 can specifically be used to: sort each language and the number of second users corresponding to each language and the session - related information according to the number of second users corresponding to each language and / or the session - related information; obtain the cross - language chat portrait information according to the sorted each language and the number of second users corresponding to each language and the session - related information.

[0056] In some embodiments, the target operation includes a first operation and a second operation. The cross-lingual chat portrait information is linked to a vocabulary set corresponding to the language of the second user. Each vocabulary in the vocabulary set is linked to a set of conversation texts corresponding to the vocabulary. The set of conversation texts includes the original chat conversation text containing the vocabulary and / or the translated chat conversation text containing the vocabulary.

[0057] The operation execution module 403 may be specifically configured to: in response to the first operation indicating the target language, display the vocabulary set corresponding to the target language, where the vocabulary set further includes the vocabulary statistics information and function controls corresponding to the vocabulary; in response to the second operation indicating the target vocabulary, display the set of conversation texts corresponding to the target vocabulary.

[0058] In some embodiments, the data processing device 400 based on cross-lingual chat may further include a vocabulary set generation module. The vocabulary set generation module may be configured to: parse the original chat conversation text and the translated chat conversation text corresponding to the same language in the conversation log information to obtain the keywords in the original chat conversation text and the keywords in the translated chat conversation text; collect the keywords in the original chat conversation text and the keywords in the translated chat conversation text to form a vocabulary set corresponding to each language. The keywords are obtained based on one or more of the following methods: word frequency statistics, term frequency-inverse document frequency statistics, word vector clustering, graph sorting extraction, deep learning extraction.

[0059] In some embodiments, the operation execution module 403 may further be configured to: in response to the first user's hiding operation on any vocabulary in the vocabulary set, hide the vocabulary indicated by the hiding operation in the vocabulary set and store the vocabulary indicated by the hiding operation in the hidden vocabulary set.

[0060] In some examples, the set of conversation texts further includes one or more of the following: text type, the conversation participating users corresponding to the text, and the text sending time, where the text type includes the original text and the translated text.

[0061] In some examples, the information acquisition module 401 may be specifically configured to: periodically acquire the conversation log information of the chat conversation where the first user is located according to a preset statistical period; or, in response to the input operation of the first user, acquire the conversation log information of the chat conversation where the first user is located within the time period indicated by the input operation.

[0062] It should be noted that the data processing device 400 based on cross-lingual chat is a device corresponding to the above-mentioned cross-lingual chat-based data processing method. All implementation manners in the above method embodiments are applicable to the embodiments of this device and can achieve the same technical effects.

[0063] This application also provides a data processing device based on cross-lingual chat. Figure 9The structural schematic diagram of the data processing device based on cross - language chat provided by an embodiment of the present application is as follows. Figure 9 As shown, the data processing device 500 based on cross - language chat includes a memory 501, a processor 502, and a computer program stored on the memory 501 and operable on the processor 502.

[0064] In some examples, the above - mentioned processor 502 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0065] The memory 501 may include a read - only memory (ROM), a random access memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical, or other physical / tangible memory storage device. Thus, generally, the memory includes one or more tangible (non - transitory) computer - readable storage media (e.g., memory devices) encoded with software including computer - executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the cross - language chat - based data processing method according to the embodiments of the present application.

[0066] The processor 502 runs the computer program corresponding to the executable program code by reading the executable program code stored in the memory 501, so as to implement the cross - language chat - based data processing method in the above - mentioned embodiment.

[0067] In some examples, the data processing device 500 based on cross - language chat may further include a communication interface 503 and a bus 504. Among them, as Figure 9 shown, the memory 501, the processor 502, and the communication interface 503 are connected through the bus 504 and complete communication with each other.

[0068] The communication interface 503 is mainly used to implement communication between each module, device, unit, and / or device in the embodiments of the present application. The input device and / or output device may also be accessed through the communication interface 503.

[0069] The bus 504 includes hardware, software, or both, and couples the components of the cross-lingual chat-based data processing device 500 to each other. By way of example and not limitation, the bus 504 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-E) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses or a combination of two or more of these. Where appropriate, the bus 504 may include one or more buses. Although the embodiments of the present application describe and illustrate specific buses, the present application contemplates any suitable bus or interconnect.

[0070] The present application also provides a computer-readable storage medium having computer program instructions stored thereon, and when the computer program instructions are executed by a processor, the data processing method based on cross-lingual chat in the above embodiments can be implemented, and the same technical effects can be achieved. To avoid repetition, it will not be elaborated here. Among them, the above computer-readable storage medium may include a non-transitory computer-readable storage medium, such as a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disc, etc., which is not limited herein.

[0071] The present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the data processing method based on cross-lingual chat in the above embodiments can be implemented, and the same technical effects can be achieved. To avoid repetition, it will not be elaborated here.

[0072] It should be clear that the various embodiments in this specification are described in a progressive manner. For the same or similar parts among the various embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. For the device embodiments, equipment embodiments, computer-readable storage medium embodiments, and computer program product embodiments, the relevant parts can refer to the description part of the method embodiments. The present application is not limited to the specific steps and structures described above and shown in the figures. Those skilled in the art can make various changes, modifications, and additions, or change the order of the steps after understanding the spirit of the present application. And, for the sake of brevity, the detailed description of known method technologies is omitted here.

[0073] The above has described aspects of the present application with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present application. It should be understood that each block in the flowchart and / or block diagram, and the combination of blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to generate a machine, such that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the functions / actions specified in one or more blocks of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It is also understood that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can also be implemented by dedicated hardware that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0074] Those skilled in the art should understand that the above embodiments are all exemplary rather than restrictive. Different technical features appearing in different embodiments can be combined to achieve beneficial effects. Those skilled in the art should be able to understand and implement other variant embodiments of the disclosed embodiments based on the study of the drawings, the specification, and the claims. In the claims, the term "comprising" does not exclude other devices or steps; the quantifier "one" does not exclude a plurality; the terms "first" and "second" are used to label names rather than to indicate any specific order. Any reference signs in the claims should not be construed as limiting the scope of protection. The functions of multiple parts in the claims can be implemented by a single hardware or software module. The fact that certain technical features appear in different dependent claims does not mean that these technical features cannot be combined to achieve beneficial effects.

Claims

1. A data processing method based on cross-language chat, characterized in that: include: Acquire session log information of a chat session in which a first user is present, the session log information including session participating users in the chat session, original text of a chat session of the session participating users, translated text of a chat session of the session participating users, and language information of the session participating users, the session participating users including the first user and a second user, the language of the first user is different from the language of the second user, and the chat session of the first user includes the original text of the chat session and the translated text of the chat session in the same language as the first user; Based on the conversation log information, cross-language chat portrait information of the chat conversation of the first user is obtained, wherein the cross-language chat portrait information is associated with a conversation text set corresponding to the language of the second user, and the conversation text set includes an original text of the chat conversation and / or a translated text of the chat conversation; In response to a target operation indicating a target language for the cross-language chat portrait information, a conversation text set corresponding to the target language is displayed.

2. The method according to claim 1, characterized in that The obtaining cross-language chat portrait information of the chat session of the first user based on the session log information includes: According to the language information of the users participating in the conversation in the conversation log information, counting the number of the second users corresponding to different languages ​​and conversation related information; The cross-language chat portrait information is obtained based on each language and the number of the second users corresponding to each language and the session related information.

3. The method according to claim 2, characterized in that The obtaining of the cross-language chat portrait information according to the languages ​​and the number of the second users corresponding to the languages ​​and the session related information includes: sorting the languages ​​and the number of the second users and the session related information corresponding to the languages ​​according to the number of the second users and / or the session related information corresponding to the languages; The cross-language chat portrait information is obtained according to the sorted languages ​​and the number of the second users corresponding to each language and the session-related information.

4. The method according to claim 1, characterized in that: The target operation includes a first operation and a second operation, the cross-language chat portrait information is linked to a vocabulary set corresponding to the language of the second user, each vocabulary in the vocabulary set is linked to a conversation text set corresponding to the vocabulary, and the conversation text set includes an original text of a chat session containing the vocabulary and / or a translated text of a chat session containing the vocabulary; The step of displaying a conversation text set corresponding to the target language in response to the target operation indicating the target language of the cross-language chat portrait information includes: In response to a first operation indicating a target language, displaying a vocabulary set corresponding to the target language, the vocabulary set also including vocabulary statistics and function controls corresponding to the vocabulary; In response to a second operation indicating a target vocabulary, a conversation text set corresponding to the target vocabulary is displayed.

5. The method according to claim 4, characterized in that Also includes: Parsing the chat session original text and the chat session translated text corresponding to the same language in the chat log information to obtain keywords in the chat session original text and keywords in the chat session translated text; Gathering keywords in the original text of the chat session and keywords in the translated text of the chat session to form a vocabulary set corresponding to each language; Among them, the keywords are obtained based on one or more of the following methods: word frequency statistics, word frequency-inverse document frequency statistics, word vector clustering, graph ranking extraction, and deep learning extraction.

6. The method according to claim 4, characterized in that Also includes: In response to the first user's hiding operation on any word in the vocabulary set, the word indicated by the hiding operation is hidden in the vocabulary set, and the word indicated by the hiding operation is stored in the hidden vocabulary set.

7. The method according to claim 1, characterized in that The conversation text collection also includes one or more of the following: Text type, conversation participant corresponding to the text, and text sending time, where the text type includes original text and translated text.

8. The method according to claim 1, characterized in that The acquiring of the session log information of the chat session of the first user includes: Periodically obtaining the session log information of the chat session of the first user according to a preset statistical period; or, In response to an input operation of the first user, the session log information of the chat session in which the first user is present within a time period indicated by the input operation is acquired.

9. A data processing device based on cross-language chat, characterized in that: include: an information acquisition module, configured to acquire session log information of a chat session in which a first user is present, the session log information including session participating users in the chat session, original text of the chat session of the session participating users, translated text of the chat session of the session participating users, and language information of the session participating users, the session participating users including the first user and a second user, the language of the first user is different from the language of the second user, and the chat session of the first user includes the original text of the chat session and the translated text of the chat session in the same language as the first user; A portrait generation module, configured to obtain cross-language chat portrait information of the chat session of the first user based on the session log information, wherein the cross-language chat portrait information is associated with a conversation text set corresponding to the language of the second user, the conversation text set including an original text of the chat session and / or a translated text of the chat session; An operation execution module is used to respond to a target operation indicating a target language for the cross-language chat portrait information and display a conversation text set corresponding to the target language.

10. A data processing device based on cross-language chat, characterized in that: include: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the data processing method based on cross-language chat as described in any one of claims 1 to 8 is implemented.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by the processor, the data processing method based on cross-language chat as described in any one of claims 1 to 8 is implemented.

12. A computer program product, characterized in that It includes a computer program, which, when executed by a processor, implements the data processing method based on cross-language chat as described in any one of claims 1 to 8.

Images