92 results about "Machine translated" patented technology

The invention relates to the technical field of machine translation, and discloses a multi-modal machine translation method and system based on a large model thinking chain. The method comprises the steps of obtaining a to-be-translated source language text and multi-modal data associated with the to-be-translated source language text; determining a text feature vector corresponding to the to-be-translated source language text and determining a multi-modal feature vector corresponding to the multi-modal data; performing cross-modal feature fusion on the text feature vector and the multi-modal feature vector based on a preset language model to obtain a multi-modal fusion feature, and determining a reasoning path corresponding to a cross-modal feature fusion process; and determining a target translation result based on the multi-modal fusion feature, the target translation result comprising a target language text, and determining a thinking chain derivation process corresponding to the target language text based on the reasoning path. The interpretability of the translation result is enhanced, a user can conveniently understand the basis and process of translation, and the quality and reliability of translation are remarkably improved.

Automatic post-editing of machine translated content using generative AI models is disclosed herein. An example method includes presenting machine translated segments of a document and their associated quality estimation scores, invoking an automated post-editing system for segments with unsatisfactory translation quality, inputting the segments into a generative AI model alongside contextual information, the contextual information comprising a variable window of text adjacent to each of the machine translated segments with unsatisfactory quality estimation scores; producing a revised translation of the segment using the generative AI model and iterating the generative AI process with varying input until a final translation is achieved or a predetermined number of attempts are reached.

The invention provides a large model context learning machine translation method based on word granularity alignment, which relates to the field of natural language processing, and comprises the following steps: an external knowledge assisting stage: performing multi-level retrieval matching on a source text word alignment set; a large model translation stage: obtaining a large model translation set, and taking the large model translation set as one of candidate translations; in the post-selection stage, the obtained source text word alignment set, the obtained external dictionary word alignment set, the obtained entity library alignment set, the obtained embedding representation of the auxiliary translation set and the obtained large model translation set are subjected to similarity calculation scoring for multiple times, and screening is conducted according to similarity scores to obtain a candidate word alignment set; a prompt template is designed according to a task, a source text word alignment set and a candidate word alignment set are put into the prompt template, and a highly-aligned external word alignment set is used for large model context learning to generate an optimal translation result; according to the method, various translation errors of a large model in a low-resource environment are relieved.

Automatic post-editing of machine translated content is disclosed herein. An example method includes receiving a source document for translation, calculating semantic signatures of text chunks within the source document using an artificial intelligence (AI) model, matching the semantic signatures against a repository of previous translations, and displaying a document-based visualization that includes both semantic and exact matches found in the repository of previous translations.

The invention discloses a text translation method and device, electronic equipment and a storage medium, and the method comprises the steps: translating an original text through employing a neural machine translation algorithm, and obtaining an initial translated text; inputting the initial translation text into a pre-constructed culture knowledge base, and outputting a content adjustment strategy corresponding to the initial translation text through the culture knowledge base; processing the initial translated text according to the content adjustment strategy to obtain an intermediate translated text, and then calling a pre-constructed policy database to verify the intermediate translated text; and generating a target translated text corresponding to the original text according to a verification result of the intermediate translated text. According to the technical scheme provided by the embodiment of the invention, the problems of cultural semantic deviation, content violation risk and the like in cross-regional translated text can be solved, ambiguity in the translated text is eliminated, and the accuracy and flexibility of a text translation result are improved.

The present application provides a method for machine translation and an electronic device, which relate to fields such as artificial intelligence (AI), natural language processing, and machine translation. The method includes: the electronic device displays a first user interface, and the original text content is displayed on the first user interface; after the electronic device detects an operation by the user to trigger scrolling screenshot, it automatically starts scrolling screenshot; the electronic device obtains a first picture through scrolling screenshot; the electronic device obtains the translated text content corresponding to the original text content displayed on the first picture; the electronic device automatically displays a second user interface, and a part or all of the translated text content is displayed on the second user interface. The embodiments of the present application help to improve the intelligence level of the electronic device (such as a smart terminal device, such as a mobile phone), and at the same time can enhance the user experience when the user views the translated text.

A speech-to-speech translation method comprises transcribing speech spoken in a source language into transcribed text data in the source language using an on-premises speech recognition model. The transcribed text data is translated into translated text data in a target language using a first on-premises machine translation model. The translated text data is reverse translated into retranslated text data in the source language using a second, different on-premises machine translation model. The transcribed text and the retranslated text are displayed on a screen. The method also involves synthesizing, using an on-premises speech synthesis model, translated speech data in the target language based on the translated text data and play back, in response to a user confirmation, translated speech in the target language based on the translated speech data in the target language.

The invention discloses a method for translating texts in batches, which relates to the technical field of automobile technology, and comprises the following translating steps: S1, acquiring terminologies in the automobile industry, and constructing an automobile translation vocabulary library; s2, performing format unification, title information recognition and error correction processing on a to-be-translated text, and judging content features of the text; and S3, according to the features of the to-be-translated text and the target language, selecting an optimal machine translation engine, optimizing the selected translation engine, docking the automobile translation vocabulary library with the translation engine, and preferentially matching professional vocabularies of the automobile translation vocabulary library by the translation engine in the translation process for translation processing. According to the method, a deep learning language model and rule evaluation are utilized; according to the method, efficient and accurate batch translation of the texts in the automobile industry is realized, the translation quality and the processing efficiency are improved, multi-aspect requirements of automobile enterprises are met, the batch translation efficiency is improved, the conditions of translation errors and inaccurate translation are reduced, and the translation quality is improved.

The invention belongs to the technical field of natural language processing and machine translation, and discloses a machine translation style consistency repairing method and related device.The machine translation style consistency repairing method comprises the steps that style labels and confidence scores of an original text and a translated text are obtained; according to the style labels and the confidence scores of the original text and the translated text, obtaining a style consistency score of the original text and the translated text, and when the style consistency score is smaller than a preset style consistency threshold value, taking the translated text as a to-be-repaired translated text; otherwise, outputting the translated text; and iterating the repairing step until the semantic similarity score of the current to-be-repaired translated text and the original text is not less than a preset semantic similarity threshold value and the style consistency score of the current to-be-repaired translated text and the original text is not less than a preset style consistency threshold value, and outputting the current to-be-repaired translated text as a repaired translated text. The method can effectively improve the consistency of translation styles, keeps semantic integrity, and is suitable for scenes with high text style requirements.

A kind of based on magnetic attraction wearing language conversion machine translation device and method, belong to machine translation technical field, to solve the operation of the existing translation device generally is suitable for face-to-face communication when translating, translation work is more cumbersome when online, the present application passes through translation control terminal and includes input module, input module electrically connected conversion selection module, conversion selection module electrically connected language conversion module, language conversion module electrically connected output module, input module electrically connected communication equipment, when carrying out translation work, utilize input module can be through voice input or text input mode input text, then utilize conversion selection module selects the kind needed to be translated, again utilize language conversion module conversion text, finally utilize output module through text output and voice output mode presents translation result, and utilize communication equipment bluetooth connection input module, can realize the purpose of real-time translation when user voice communication.

The application is suitable for the technical field of machine translation, and provides a machine translation method and device based on imitation learning and terminal equipment, the method comprising: inputting a to-be-translated original text into a non-autoregressive model to obtain a first initial translation; when the first initial translation passes through a deletion classification decoder, deleting an error word in the first initial translation, and outputting a second initial translation; when the second initial translation passes through an insertion classification decoder, using a placeholder to fill a missing word position in the second initial translation, and outputting a third initial translation; and when the third initial translation passes through a word classification decoder, replacing the placeholder with a correct word, and outputting a final translation. The application combines the advantages of autoregressive models and non-autoregressive models, and can realize parallel acceleration while ensuring the quality of the translation.

This disclosure solves various technological problems described above by using large language models to improve machine translations for proper usage of terminology, gender, or other suitable predetermined linguistics. Such improvements may be manifested by resolution of certain grammar, morphology, or other linguistic issues in machine-translation outputs incorporating specific terminology, such as brand names and gendered terms, as disclosed herein.

The present disclosure provides a training method and apparatus for a machine translation model. The training method includes: obtaining a text to be translated and an expected translation of the text to be translated for a specific user; obtaining a first coded expression of the text to be translated, and obtaining a second coded expression for characterizing the language characteristics of the specific user; obtaining a third coded expression input to the machine translation model and a translation result of the text to be translated based on the first coded expression and the second coded expression; calculating a loss value based on the translation result of the text to be translated and the expected translation of the text to be translated; if the loss value is greater than or equal to a preset threshold, adjusting the weight parameters of the machine translation model based on the loss value; and stopping the training method if the loss value is less than the preset threshold. In this method, the second coded expression characterizing the language characteristics of the specific user is used to adjust the coded expression input to the machine translation model to provide personalized translation results.

The present application relates to a large model Lao translation method and system based on agent cooperation and cross-language semantic alignment, belonging to the field of natural language processing and machine translation. In the independent multilingual translation generation stage, the target Lao translation corresponding to the source sentence, the reference language translation and the linguistically similar language translation are generated respectively; cross-language semantic alignment and correction are carried out, the English translation is taken as the semantic alignment reference, the Thai translation provides morphological and syntactic guidance, and through the iterative information interaction between agents, the Lao translation is continuously evaluated and corrected; in the semantic clarification and strengthening stage, the key semantic fragments of the source sentence are extracted by analyzing the agent, the overall structure is simplified, and the Lao agent is assisted to generate the final high-fidelity translation result. The present application significantly improves the semantic accuracy and structural rationality of the Lao translation result; effectively solves the semantic illusion problem easily produced by the large language model in the Lao machine translation scene due to data scarcity.

The invention relates to a data enhancement method based on a two-way translation adversarial network, and belongs to the field of natural language processing. In low-resource language machine translation, the problem that a machine translation result deviates in a specific field due to the lack of large-scale diversified training corpora is solved. In order to relieve the influence caused by deviation in a specific field, the invention provides the method, and the method combines adversarial training and a bidirectional back translation technology, and generates a high-quality adversarial sample and diversified training data by using a pre-trained shielded language model so as to enhance the robustness of the model. Firstly, adversarial training is used for carrying out data enhancement on a data set, then a bidirectional translation method is used for generating texts with similar semantics but different expressions, then a pre-training shielding language model is used for generating text variants with reasonable semantics, finally, bidirectional translation and MLM are fused to generate more reasonable and diversified enhanced data, and the data are used for retraining the model. According to the method, the robustness and the performance of the NMT model are remarkably enhanced on the data level.

The embodiments of this disclosure relate to post-editing methods and apparatuses, electronic devices, and storage media, and pertain to the field of machine translation technology. The post-editing method includes: acquiring a target source text and a target machine-translated text, wherein the target machine-translated text is the machine-translated version of the target source text; inputting the target source text and the target machine-translated text into a pre-trained post-editing model, and correcting the target machine-translated text using the post-editing model; the correction methods of the post-editing model include deleting tokens, inserting placeholders, and replacing placeholders with tokens; and outputting the target post-edited text obtained by the post-editing model correcting the target machine-translated text.

An electronic apparatus includes a memory configured to store instructions, and a processor configured to execute the instructions to receive a text of a first language, generate, based on the text of the first language, style information indicating a translation style to be applied to the text of the first language and machine-translate the text of the first language into text of a second language based on the generated style information.

The invention discloses a multilingual off-line simultaneous interpretation method based on a small bidirectional translation model. According to the method, voice front-end enhancement, automatic voice recognition, neural machine translation and text-to-voice conversion are sequentially executed locally on a terminal, and a unified sub-word list and a shared model are adopted to automatically judge translation between at least two languages and generate a target text / voice in a stream mode. In order to adapt to the mobile terminal, the translation model is quantized by an integer and deployed in a lightweight format, and the time delay is reduced in combination with beam width search, early stop and cache mechanisms. The voice front end adopts a causal U-shaped network to jointly suppress reverberation and noise, room impulse response and multi-noise are introduced in the training stage, a signal-to-noise ratio strategy is configured, and time sequence weighted filtering and mean square error gain are combined in the reasoning stage to improve intelligibility. The device is composed of a mobile terminal and a Bluetooth earphone, supports a collaborative chain of earphone side front enhancement and mobile phone side decoding, and realizes stable simultaneous transmission output in a weak network / no network scene.

The present invention is a method for automatic post-editing of machine translation based on large model data augmentation. The present invention relates to the technical fields of automatic post-editing of machine translation and data augmentation. The present invention generates a large amount of pseudo-data that can be used for training through domain screening and forward translation, and generates additional auxiliary machine translation translations with the help of a large language model to solve the problem of data scarcity faced by the automatic post-editing task. Then, all the data obtained after data augmentation is input into the cross-lingual pre-trained model mBART for training, effectively improving the quality of machine translation translations. The method proposed by the present invention reasonably utilizes the language ability of the large language model, can simply and efficiently solve the problem of data scarcity faced by the automatic post-editing task, and at the same time, this method can be directly applied to the automatic post-editing task on multiple language pairs without having to train multiple machine translation models for data augmentation on different language pairs.

The invention relates to the technical field of machine translation and the like, and provides a multi-scene intelligent desktop translation machine which comprises a host and a detachable translation bar. The host receives and processes an instruction of the mobile intelligent terminal; the host shell is provided with a translation rod configuration groove and a charging head, and the translation rod can be charged and a pickup of the translation rod is turned off when the translation rod is assembled in the groove; in a noisy environment, a user can take down the translation rod for short-distance pickup and translation playing. The host is provided with a distributed pickup array, and when the translation bar pickup is turned off, sounds in different directions are positioned, and the voice of a main speaker is selected and extracted to translate and play. And the host monitors the connection state of the translation rod and the charging head, and closes the sound pickup array when the translation rod and the charging head are disconnected, so that automatic switching between a near-speaking mode of the translation rod and a far-speaking mode of the host is realized. The method can solve the problems that a desktop translation machine is poor in hardware expansibility, and a noisy scene pickup path is not adaptive, and realizes intelligent switching of pickup paths and multi-scene adaptation based on a hardware structure and state perception.

This invention discloses a machine translation hallucination detection method based on a large language model. The method comprises the following steps: selecting a pre-defined large language model prompt template according to the translation direction; establishing word alignment relationships between the machine translation and the source language sentence; searching for semantically matched, semantically conflicting, and semantically missing word pairs between the machine translation and the source language sentence; using feedback information from the error response sample set to perform multi-dimensional risk scoring for machine translation hallucination detection and automatically optimizing the large language model prompt template; performing a seven-dimensional machine translation hallucination risk probability score, namely, semantic addition risk probability, syntactic logic addition risk probability, format noise risk probability, over-translation risk probability, factual error risk probability, implicit fiction risk probability, and fabrication information risk probability; and summarizing the seven-dimensional machine translation hallucination risk probability scores to predict whether machine translation hallucination exists in the machine translation.

The invention relates to an evaluation set construction method and device of a translation model, computer equipment, a computer readable storage medium and a computer program product. The method relates to the field of machine translation in a natural language processing technology, and comprises the following steps: obtaining candidate description words of a source language, and screening out fuzzy attitude words from the candidate description words according to emotional attitude scores of the candidate description words; obtaining a character description statement which adopts the fuzzy attitude word to describe the character as a source statement; obtaining character anonyms of source languages corresponding to the multiple non-binary gender identity types; and respectively replacing the character anonyms in the source statements with the character anonyms of the source languages corresponding to the non-binary gender identity types to obtain source statements related to multivariate genders, and using the source statements related to the multivariate genders to generate an evaluation set for performing gender prejudice evaluation on the translation model. The evaluation set obtained by adopting the method can be used for effectively presenting the gender prejudice of the translation model to the non-binary gender with high quality.

The application discloses a machine translation method and device, the method comprises the following steps: receiving a source text in a source language; inputting the source text into an encoder, and generating an encoding output of the encoder by using hidden representations output by each sub-encoder in the encoder, wherein each sub-encoder comprises at least one neural network layer; and performing decoding on the encoding output by using a decoder to obtain a target text in a target language. The application adopts the mode of splitting the encoder and using multiple sub-encoders to jointly generate the encoding output, so that the encoding output can represent more text features of the source text, that is, the encoding output comprising low-level semantic information and high-level syntax information is provided to the decoder for processing, thereby more accurately translating the source text.

To provide a translation support device and program capable of automatically generating at a high yield a translation memory and a term base that do not require artificial operation, have high versatility, and can be quickly acquired in a wide range of fields.SOLUTION: A translation support device acquires original text data in a first language, which is to be translated, and translated text data in a second language, which is a result of translating the original text data, divides the original text data into predetermined segment units and machine-translates at least some of segments obtained by the division into the second language to generate a preliminary translation memory from data obtained by reversing a language direction of a sentence pair related to the segments of the original text data and the machine translation result, divides the acquired translated data of the second language into predetermined segment units to execute pre-translation by the generated preliminary translation memory, and automatically generates a translation memory on the basis of the original text data of the second language and the sentence pair obtained from the translation result by the preliminary translation memory.SELECTED DRAWING: Figure 1

Provided are computer-implemented systems and methods for providing improved rule-based machine translation, including a memory having a plurality of text translations, each text translation comprising an input text element and an output text element; at a processor: generating a text translation cluster comprising a plurality of matching text translations in the plurality of text translations, the text translation cluster associating the plurality of matching text translations; receiving the plurality of matching text translations associated with the text translation cluster; generating a first translation rule corresponding to the input text elements of the text translation cluster and a second translation rule corresponding to the output text elements; generating a machine translation rule based on the first translation rule and the second translation rule, the machine translation rule comprising a template and at least one converter; receiving an input text element for translation; and determining an output translated text element.

The invention relates to a two-stage semantic aggregation method for specific field multi-modal neural machine translation, and belongs to the technical field of natural language processing. The method comprises the following steps of: obtaining embedding characterization of a text and vision; text embedded representation is sent to a traditional Transform model for a first stage, and representation and translation capabilities of general semantics are integrally learned to lay a foundation; carrying out second-stage incremental learning by adopting a comparison decoupling strategy and a multi-level collaborative decoding mechanism, and focusing on specific representation and translation capabilities in the learning field; carrying out consistency constraint on the text representation of the first stage and the text representation of the second stage through a dual-stage representation mild optimization strategy; and the KL divergence and the traditional cross entropy loss are combined as a final training target. According to the method, the problem of fine-grained imbalance of domain graphic and text information in the aspect of domain specific characteristics caused by high domain term specialty, low occurrence frequency and sparse distribution is solved.

The invention relates to a Vietnamese text image translation method based on fine-grained contrast learning, and belongs to the field of natural language processing. A Vietnamese text contains a large number of characters with tones and note changing numbers, and in order to solve the problem that translation accuracy of similar characters in a Vietnamese image is low, the invention provides a fine-grained contrast learning-based Vietnamese text image translation method. The method mainly comprises the steps of constructing and preprocessing a Vietnamese text image translation data set, and designing a Vietnamese text image translation model and Vietnamese text image translation, wherein the Vietnamese text image translation model comprises an end-to-end text image machine translation module, an auxiliary machine translation task module and a fine-grained contrast learning module. The method provided by the invention is excellent in performance in Vietnamese text image translation tasks, and the translation accuracy in a low-quality image scene is also obviously improved.

The invention provides a traditional medicine AI translation engine, which converts literatures such as traditional Chinese medicine and Ayurbark from a concept space to a modern medicine semantic space. A neural machine translation and semantic mapping combined double-stage method is adopted, ancient Chinese, Sanskrit and the like are translated firstly, then a traditional concept is automatically annotated as a modern term (such as liver depression and qi stagnation)-liver dysfunction caused by emotion depression), and a knowledge graph associated entity is constructed. And multi-language and multi-format output is supported, and semantic annotation teaching materials or computable knowledge data are generated, so that non-traditional Chinese medicine background researchers can understand medical cases and take the medical cases into evidence-based research. International propagation and academic mining of traditional medicine are promoted, traditional Chinese medicine digitization is assisted, and great strategic significance is achieved.

The embodiment of the present invention discloses a model training, machine translation method, device, equipment and storage medium. The model training method may include: obtaining original parallel corpus including original source end data and original target end data; using the original source end data as exchange target end data, and using the original target end data as exchange source end data to obtain exchange parallel corpus, and training the original translation model based on multiple groups of original parallel corpora and multiple groups of exchange parallel corpora to obtain an intermediate translation model; training the intermediate translation model based on multiple groups of original parallel corpora to obtain a machine translation model. The technical solution of the embodiment of the present invention can improve the training effect of the machine translation model.

To provide a translation support device and a program for improving the efficiency of translation using machine translation software.SOLUTION: A translation support device divides a source text to be translated that is written in a first language into predetermined segments, generates a translated text in a second language that is different from the first language for each of the segments, associates the source text for each of the segments with the corresponding translated text and outputs the associated source text and translated text as parallel translation information, outputs, to a large scale language model, the parallel translation information and revision instruction information that instructs revision of the parallel translation information, acquires an output from the large scale language model, and generates revised parallel translation information.SELECTED DRAWING: Figure 1