37 results about "Machine translated" patented technology

Machine translation decoding is accomplished by receiving as input a text segment in a source language to be translated into a target language, generating an initial translation as a current target language translation, applying one or more modification operators to the current target language translation to generate one or more modified target language translations, determining whether one or more of the modified target language translations represents an improved translation in comparison with the current target language translation, setting a modified target language translation as the current target language translation, and repeating these steps until occurrence of a termination condition. Automatically generating a tree (e.g., either a syntactic tree or a discourse tree) can be accomplished by receiving as input a tree corresponding to a source language text segment, and applying one or more decision rules to the received input to generate a tree corresponding to a target language text segment.

The present invention is a system of integrated, computer-based processes for monolingual information development. and multilingual translation. An interactive text editor enforces lexical and grammatical constraints on a natural language subset used by the authors to create their text, which they help disambiguate to ensure translatability. The resulting translatable source language text undergoes machine translation into any one of a set of target languages, without the translated text requiring any postediting.

The invention is a kind of Chinese and foreign languages double-way automatic translation method which is based on the sentential form conversion and adopts the Chinese phonetic codes with the word as the unit, and it belongs to the automatic translation technology field. By the method we can translate Chinese and another language such as English to each other expediently, on one hand, it overcomes the present faults that all the Chinese-foreign languages translation can only be done by the Chinese characters and Chinese spelling because of that Chinese uses the connecting written Chinese phonetic codes by word which only adopts twenty-six Latin letters as the code element and includes Chinese sound, rhythm and tone to expresses information when translating, that Chinese characters and Chinese phonetic signs can not be compatible with ASCII codes completely and the malpractice that before every translation, the Chinese information that Chinese characters expresses has to be divided to single word, on the other hand, because of adopting the sentential form conversion method which bases on the lexical and the syntax system coincident with the goal language, to make the automatic translation method more exact than the traditional translation method, and the effectiveness better. However the inputting Chinese form is Chinese characters, Chinese spelling or Chinese phonetic codes, it can not only translates by the traditional method but also translate after changing the Chinese characters to Chinese phonetic codes, likely, if needed, the Chinese phonetic codes gotten after translation can not only express the Chinese information directly, but also be conversed to Chinese characters, Chinese spelling, Chinese sound, Chinese special person, Chinese localism or minority language sound.

The invention provides a method and a device for calculating sentence similarity and a method and a device for machine translation, wherein the method for calculating the sentence similarity comprises the following steps that a first sentence and a second sentence are compared, so as to determine different word pairs; different words are marked by utilizing the matching probability of the different words in the different word pairs with other words in the first sentence or the second sentence in which the different words are contained, wherein the matching probability of two words is obtained by inquiring a matching probability model, and the matching probability of the two words in the matching probability model is obtained by counting the co-occurrence frequency of the two words in a preset corpus; the marking results of the different words in the different word pairs are utilized to mark the different word pairs; and the marking results of the different word pairs are utilized to determine the similarity of the first sentence and the second sentence. According to the method and the device, the matching degree of the two sentences can be more accurately reflected, thereby increasing the application quality of the method and the device for the machine translation and the like.

Described herein are systems and methods for generating natural language sentences with Sequence-to-sequence (Seq2Seq) models with attention. The Seq2Seq models may be implemented in applications, such as machine translation, image captioning, and speech recognition. Performance has further been improved by leveraging unlabeled data, often in the form of a language models. Disclosed herein are Cold Fusion architecture embodiments that leverage a pre-trained language model during training. The Seq2Seq models with Cold Fusion embodiments are able to better utilize language information enjoying faster convergence, better generalization, and almost complete transfer to a new domain while using less labeled training data.

The invention relates to an audio-video recording and broadcasting device capable of translating speeches and marking subtitles automatically in real time for Chinese and foreign languages, and belongs to the technical field of voice and picture data processing equipment. The device comprises a microphone, a camera module 1, an audio-video synchronization signal marking module 2, a vocal language audio signal extraction module 3, a Chinese speech identification module 4, a machine translation module 5 for translating Chinese into foreign languages, an audio picture or an image picture subtitle stacking machine module 6, a foreign language speech synthesis module 7, an audio-video coding and compressing module 8, a network transmission module 9, a server module 10 provided with an audio-video decoding and decompressing software, a network transmission module 11, a client module 12 provided with audio-video play software, a foreign language speech identification module 13, a machine translation module 14 for translating the foreign languages into the Chinese and a Chinese speech synthesis module 15. The audio-video recording and broadcasting device can facilitate real-time communication of Chinese and foreign information.

The invention discloses a word and expression alignment method and device and relates to the technical field of machine translation. The word and expression alignment method and device achieve the technical goal of word and expression alignment. According to the technical scheme, the method comprises the steps of segmenting sentences, needing to be aligned, of two languages into words or expressions one by one, carrying out word and expression grouping, carrying out query matching in word and expression groups, and carrying out alignment of intertranslation pairs. The word and expression alignment method and device are used for accurate and complete building of a phrase list in the machine translation process.

The invention relates to a business logic code-free development method based on natural language understanding and conversion, and belongs to the technical field of code-free development. On one hand, development semantic extraction driven by natural language understanding is carried out; key development elements in the natural language description are automatically extracted to serve as a sequence labeling problem to be processed; and supervised training is performed on massive tagged code-free development demand corpora described by natural languages to obtain a development semantic extraction model. On the other hand, a natural language code generation task is is performed; after the intention of the user is analyzed, the metadata is sent into a model engine, and a corresponding code generation model is called by the model engine; and the code generation task is converted into a machine translation task. The method can reduce the software development and maintenance cost, improve the development efficiency, reduce the influence of demand change on the system and prolong the life cycle of the system.

The invention discloses a Japanese verb identification method for machine translation and belongs to the field of natural language processing. The method has the advantages that the method is based on rules and combines with dictionaries by analyzing the conjugated form rules of Japanese verbs, the verbs in a text can be identified completely, and basic forms of the verbs can be obtained by form restoration; the method can use common universal dictionaries and is high in adaptability and robustness; by the method, the lexical analysis accuracy and bilingual word corresponding effect in machine translation are increased, and the translation quality of machine translation is increased as a whole.

The invention discloses a translation detection method and device and an electronic device, belonging to the technical field of language processing. The translation detection method comprises the following steps of obtaining a first translation, wherein the first translation is translated by an interpreter according to the original text; acquiring a plurality of second translations, the pluralityof second translations being translated for the original text by different machine translation engines; calculating a degree of matching between the first translation and the plurality of second translations; determining a target second translation according to the matching degree; determining the detection result of the first translation according to the target second translation. The invention determines whether the first translation has the translation result of the reference machine translation engine according to the target second translation, thereby effectively improving the reliabilityof the translation in the translation evaluation set, avoiding the occurrence of the unqualified translation in the translation evaluation set, facilitating the cost saving, and realizing the batch detection of the translation translated by the interpreter.

The invention discloses a machine translation model training method and device and a storage medium and belongs to the technical field of natural language processing. The method comprises the following steps: acquiring a multi-domain mixed training data set containing a plurality of training data pairs; performing data domain classification on the plurality of training data pairs to obtain at least two domain data subsets; determining at least two candidate optimization objectives of each domain data subset, and respectively training at least two candidate single domain models corresponding toeach domain data subset based on the at least two candidate optimization objectives; respectively testing at least two candidate single-domain models corresponding to each domain data subset, and selecting a candidate optimization target with the highest accuracy corresponding to the candidate single-domain model as a specified optimization target of the domain data subset; and training a hybriddomain model based on each domain data subset in the training data set and the corresponding specified optimization target. The quality of language data translation results of various fields by the hybrid field model can be improved.

The invention relates to a machine translation method for achieving the whole-sentence translation through preferential translation of idiomatic phrases composed of one or more phrase nests on the basis. According to the embodiment, the method comprises the following steps of marking idiomatic phrases, wherein idiomatic phrases in sentences in a source language are marked out; translating the idiomatic phrases, wherein the idiomatic phrases are divided into two parts to be translated respectively, and translations are regrouped; dividing short phrases, wherein remaining parts of original sentences are divided into all possible phrases, and the idiomatic parts are adopted as translated portions; constructing a candidate phrase list, wherein only phrases existing in a phrase translation probability list are screened out and added into the candidate phrase list; translating sentences, wherein the optimal translation is generated by means of an existing heuristic decoder and the candidate phrase list for the partially-translated source language sentences composed of the idiomatic translation and other non-translated parts. According to the first stage, the idiomatic phrases are translated, and according to the second stage, the remaining parts of the sentences are translated.

The invention provides a neural machine translation method based on similarity perception, and belongs to the technical field of natural language processing machine translation. The method comprises the following steps of firstly, creating a structure translation memory bank and a corresponding structure similarity algorithm; constructing a template translation memory bank and a corresponding template similarity algorithm; and then pre-identifying high-potential statements of character strings, structures and template dimensions in the test set; constructing multi-dimensional similarity prioriknowledge, and performing multi-dimensional similarity retrieval on all parallel statements in the training set. A posteriori regular objective function is utilized, discrete similarity priori knowledge is fused into a neural machine translation objective function, parameters of the priori knowledge are continuously iterated and updated, and a training process is guided. Finally, the multi-dimensional high-potential to-be-translated statements are translated by utilizing the trained neural translation model. The most similar sentences can be obtained in a finer-grained manner, and the reviewtime of human translators is shortened.

The invention discloses a method for automatically generating Chinese couplets by using a natural language processing technology. The method comprises the following steps: acquiring and processing a first line-second line data pair, a keyword-first line data pair and a horizontal streamer-first and second line data pair; performing word segmentation on all Chinese couplet data; building a machine translation model for realizing the Chinese couplet matching function, and training the model by using first line-second line data; reordering the model output according to rhymelevel and oblique tone rules, performing forced truncation on the output with a repeated translation phenomenon in the couplet matching process, and evaluating the model output; building and training a text generation model, and automatically adjusting the length of the Chinese couplet generated by using a length penalty coefficient automatic adjustment strategy; and building a text classification model for realizing a horizontal streamer generation function and training the text classification model. According to the method, the function of very complete couplet task is realized, the semantic integrating degree between the first line and second line is very high, the level and oblique correspondence is accurate, and the generated Chinese couplet is very high in relevancy with the keyword and beautiful in artistic conception.

The invention discloses a chapter-level neural machine translation method and system based on a routing algorithm, and belongs to the technical field of natural language processing application. According to the method, context information input into a model system is screened through a routing algorithm, some words useful for sentences to be translated are actively selected from the sentences to be translated to form needed chapter-level information, the information is modeled through the model by introducing the routing algorithm, and therefore the chapter-level neural machine translation effect is improved. The system comprises a data processing module, a routing algorithm module, a context encoder module, a source language encoder module, a decoder module and a generator module. Compared with the prior art, the method and system have the advantages that the anterior text and the posterior text are simultaneously used as required chapter-level information, the to-be-translated sentence automatically selects the words in the context as a part of the required information by utilizing the routing algorithm, the chapter-level neural translation model is constructed, the context information is effectively utilized, and the chapter-level machine translation quality is improved.

Techniques described herein may serve to increase the language coverage of an automated assistant system, i.e. they may serve to increase the number of queries in one or more non-native languages for which the automated assistant is able to deliver reasonable responses. For example, techniques are described herein for training and utilizing a machine translation model to map a plurality of semantically-related natural language inputs in one language to one or more canonical translations in another language. In various implementations, the canonical translations may be selected and/or optimized for determining an intent of the speaker by the automated assistant, so that one or more responsive actions can be performed based on the speaker's intent. Put another way, the canonical translations may be specifically formatted for indicating the intent of the speaker to the automated assistant.