277 results about "Machine translation system" patented technology

A method for rewriting source text includes receiving source text including a source text string in a first natural language. The source text string is translated with a machine translation system to generate a first target text string in a second natural language. A translation confidence for the source text string is computed, based on the first target text string. At least one alternative text string is generated, where possible, in the first natural language by automatically rewriting the source string. Each alternative string is translated to generate a second target text string in the second natural language. A translation confidence is computed for the alternative text string based on the second target string. Based on the computed translation confidences, one of the alternative text strings may be selected as a candidate replacement for the source text string and may be proposed to a user on a graphical user interface.

In order to achieve improvement of the accuracy and speed of a conversion of source language elements to target language elements a machine translation system is provided with context and linguistic processing comprising a dictionary storage 100, linguistic analysis storage 114, transfer rule storage 116 and a context storage 130, wherein selecting means 118 determines an order of selection among transfer rules to be executed to obtain target language elements from linguistic processing and target language elements from context processing. The correlation between language elements and context elements is obtained using a neural network.

A method for terminological adaptation includes receiving a vocabulary pair including source and target language terms. Each term is in a class which includes a set of sequences. Contextual phrase pairs are extracted from a bilingual training corpus, each including source and target phrases. The phrases each include a sequence of the same class as the respective source and target terms as well as some associated context. Templates are generated, based on the contextual phrase pairs. In each template the source and target sequences of a contextual phrase pair are replaced with respective placeholders, each denoting the respective class of the sequence. Candidate phrase pairs are generated from these templates. In each candidate phrase pair, the placeholders of one of the templates are replaced with respective terms of a vocabulary pair of the same class. Some candidate phrase pairs are incorporated into a phrase table of a machine translation system.

In one embodiment of the present invention, a decoder receives a dependency tree as a source language input and accesses a set of statistical models that produce outputs combined in a log linear framework. The decoder also accesses a table of treelet translation pairs and returns a target dependency tree based on the source dependency tree, based on access to the table of treelet translation pairs, and based on the application of the statistical models.

A computer implemented method translates a textual input in a first language to a textual output in a second language. An input logical form is generated based on the textual input. When a plurality of transfer mappings in a transfer mapping database match the input logical form (or at least a portion thereof) one or more of those plurality of matching transfer mappings is selected based on a predetermined metric. Textual output is generated based on the selected transfer logical form.

A machine translation system is trained to generate confidence scores indicative of a quality of a translation result. A source string is translated with a machine translator to generate a target string. Features indicative of translation operations performed are extracted from the machine translator. A trusted entity-assigned translation score is obtained and is indicative of a trusted entity-assigned translation quality of the translated string. A relationship between a subset of the extracted features and the trusted entity-assigned translation score is identified.

A n-gram statistical analysis is employed to acquire frequently appearing character strings of n characters or more, and individual character strings having n characters or more are replaced by character translation codes of 1 byte each. The correlation between the original character strings having n characters and the character translation codes is registered in a character translation code table. Assume that a character string of three characters, i.e., a character string of three bytes, "sta," is registered as 1-byte code "e5" and that a character string of four characters, i.e., a character string of four bytes, "tion," is registered as 1-byte code "f1." Then, the word "station," which consists of a character string of seven characters, i.e., seven bytes, is represented by the 2-byte code "e5 f1," so that this contributes to a compression of five bytes.

A machine translation system is trained to generate confidence scores indicative of a quality of a translation result. A source string is translated with a machine translator to generate a target string. Features indicative of translation operations performed are extracted from the machine translator. A trusted entity-assigned translation score is obtained and is indicative of a trusted entity-assigned translation quality of the translated string. A relationship between a subset of the extracted features and the trusted entity-assigned translation score is identified.

A computer-implemented method for providing information to an automatic machine translation system to improve translation accuracy is disclosed. The method includes receiving a collection of source text. An attempted translation that corresponds to the collection of source text is received from the automatic machine translation system. A correction input, which is configured to effectuate a correction of at least one error in the attempted translation, is also received. Finally, information is provided to the automatic machine translation system to reduce the likelihood that the error will be repeated in subsequent translations generated by the automatic machine translation system.

A system and method for machine translation are disclosed. Source sentences are received. For each source sentence, a target sentence comprising target words is generated. A plurality of translation neighbors of the target sentence is generated. Phrase alignments are computed between the source sentence and the translation neighbor. Translation neighbors are scored with a translation scoring model, based on the phrase alignment. Translation neighbors are ranked, based on the scores. In training the model, parameters of the model are updated based on an external ranking of the ranked translation neighbors. The generating of translation neighbors, scoring, ranking, and, in the case of training, updating the parameters, are iterated with one of the translation neighbors as the target sentence. In the case of decoding, one of the translation neighbors is output as a translation. The system and method may be at least partially implemented with a computer processor.

Existing statistical machine translation machines presently require the availability of a given source language text and an equivalent target language text and from target text to train a translation system. The invention proposes training a statistical machine translation system, more specifically it proposes a computer means and method for training a statistical machine translation system using unilingual source language information.

A machine translation system includes: a distributing module for distributing an input sentence to a plurality of machine translation apparatuses for generating a translation of a second language of the input sentence of a first language, and receiving the translation of the second language from each of the plurality of translation apparatuses; a translation improving module, using each of the translations of the second language received by the distributing module as a starting point, improving the translation such that an evaluation in accordance with a prescribed evaluation method is improved; and a translation selecting module for selecting, as a translation of the input sentence, a translation satisfying a prescribed condition, among the translations improved by the translation improving module.

A computer-implemented method for providing information to an automatic machine translation system to improve translation accuracy is disclosed. The method includes receiving a collection of source text. An attempted translation that corresponds to the collection of source text is received from the automatic machine translation system. A correction input, which is configured to effectuate a correction of at least one error in the attempted translation, is also received. Finally, information is provided to the automatic machine translation system to reduce the likelihood that the error will be repeated in subsequent translations generated by the automatic machine translation system.

An embodiment provides (a) a method and system for representing natural languages in a common machine-readable form, including the thorough design of the lexicon and grammar, the resulting representation called interlingua, (b) a method and system for using a computer to convert a text of a natural language into and out of a coded text of said interlingua representation, including a programming framework which is independent of other languages, said system is called interlingua engine, and (c) a method and system of machine translation using said interlingua engine, said system called interlingua machine translation system. Alternative embodiments are described.

A method of identifying and using side information available to statistical machine translation systems within an enterprise setting, the method including extracting user-specific interaction and non-interaction-based information from at least one corresponding database within the enterprise for each of a plurality of users, aggregating the user-specific interaction and non-interaction based information from a plurality of users, by using a processor on a computer, to tune and adapt background translation and language models, and updating all relevant models within the enterprise after user activity based on the tuned and adapted translation and language models.

Systems, methods, and computer media for identifying query rewriting replacement terms are provided. A list of related string pairs each comprising a first string and second string is received. The first string of each related string pair is a user search query extracted from user click log data. For one or more of the related string pairs, the string pair is provided as inputs to a statistical machine translation model. The model identifies one or more pairs of corresponding terms, each pair of corresponding terms including a first term from the first string and a second term from the second string. The model also calculates a probability of relatedness for each of the one or more pairs of corresponding terms. Term pairs whose calculated probability of relatedness exceeds a threshold are characterized as query term replacements and incorporated, along with the probability of relatedness, into a query rewriting candidate database.

A translation method comprises: retrieving a fuzzy match text segment translation pair from a translation memory (TM) for an input source language text segment, the fuzzy match text segment translation pair comprising a fuzzy source language text segment having a fuzzy match to the input source language text segment and a corresponding translated target language text segment; extracting from the fuzzy match text segment translation pair an exact match phrase pair comprising a source language phrase that exactly matches a phrase of the input source language text segment and a corresponding translated target language phrase; and invoking a statistical machine translation (SMT) system to generate a proposed translation of the input source language text segment based on a statistical translation model that is enriched by the exact match phrase pair with the exact match phrase pair assigned a high statistical probability.

A system and method suited to translation of queries are disclosed. The method includes receiving an input query in a source language and outputting a target query, based on a translation of the input query into a target language, different from the source language. The translation is performed with a machine translation system which has been trained on representations of features of translated queries that have been generated by translation of an original query, in the source language, into the target language and a measure of information retrieval performance of each the translated queries, for each of a set of original queries.

A method, an apparatus, and a computer program for measuring a quality of the machine translation. An original segment, for example one sentence in English, is translated to a target language, for example Spanish. The translated sequence is translated and then back-translated with several machine translation engines back to the original language, for example English. The resulting translations, back-translations are compared, possibly to each other, and to the original sequence. This gives measurement value of the quality of the back translations. At least one measured value from above steps of the process can be used in order to output information about the quality of the machine translation.

Systems and methods for active learning of statistical machine translation systems through dynamic creation and updating of parallel corpus. The systems and methods provided create accurate parallel corpus entries from a test set of sentences, words, phrases, etc. by calculating confidence scores for particular translations. Translations with high confidence scores are added directly to the corpus and the translations with low confidence scores are presented to human translations for corrections.

A system and method for generating word alignments from pairs of aligned text strings are provided. A corpus of text strings provides pairs of text strings, primarily sentences, in source and target languages. A first alignment between a text string pair creates links therebetween. Each link links a single token of the first text string to a single token of the second text string. A second alignment also creates links between the text string pair. In some cases, these links may correspond to bi-phrases. A modified first alignment is generated by selectively modifying links in the first alignment which include a word which is infrequent in the corpus, based on links generated in the second alignment. This results in removing at least some of the links for the infrequent words, allowing more compact and better quality bi-phrases, with higher vocabulary coverage, to be extracted for use in a machine translation system.

The present invention discloses a statistics-based machine translation method and apparatus and an electronic device, a semantic similarity-degree calculation method and apparatus and an electronic device, and a word quantization method and apparatus and an electronic device. The statistics-based machine translation method comprises: according to a feature that affects a translation probability and that is of each candidate translation and a pre-generated translation probability prediction model generating a translation probability of a sentence to be translated into each candidate translation, wherein the feature that affects the translation probability at least comprises a semantic similarity-degree between the sentence to be translated and the candidate translation; and selecting a preset number of candidate translations whose translation probabilities rank top as a translation of the sentence to be translated. By adoption of the statistics-based machine translation method provided by the present application, the semantic level of the natural language can be reached deeply when the machine translation model is constructed, and the deviation of semantics between the translation and the source text is avoided, so as to achieve the effect of improving translation quality.

The invention belongs to the technical field of computer software and discloses a multi-language-pair neural network machine translation method and system. A plurality of bilingual parallel corpora ofa same language system are utilized and mapped to a same high-dimensional vector space after byte pair encoding, so that multiple languages share a same semantic space, the size of a word list is reduced, model parameters are reduced, and convergence of a model is accelerated. Words of a same language family are in the same vector space, more information can be learned mutually, the information which can not be learned through only certain bilingual parallel corpora can be learnt, and the quality of word vectors is improved. The machine translation system can be used for translation in the language direction without direct bilingual parallel corpora, and the translation quality in the scarce parallel corpus translation direction is greatly improved through mutual information learning. Meanwhile, the same model is used for translation for the translation direction low in utilization rate, occupation of a server is reduced, and the utilization rate of the server is increased.

The present invention provides a method and apparatus for translation of a cross-language query request as well as a cross-language information retrieval method and system. The method for translation of a cross-language query request comprises: translating the cross-language query request from source language into a target language respectively with a plurality of different machine translation systems to obtain a plurality of translations in said target language of the cross-language query request; and constructing a target language query request corresponding to the cross-language query request based on said plurality of translations in said target language of the cross-language query request. The present invention constructs a target language query request by merging translations of cross-language query request generated by a plurality of different machine translation systems and hence improves the retrieval performance of cross-language information retrieval system.

Methods and apparatus for machine translation are disclosed. In one embodiment of the invention, information is stored in a memory which is contained in a computer, or some other device. The stored information includes a set of eigens for a number of languages, a cross-language eigen dictionary, a pattern dictionary, and a cross-language pattern dictionary. The first step of the translation is the conversion of a sentence in a first language to an instantiated pattern form. A corresponding pattern is then found in the cross-language pattern dictionary. Eigens are then found using the cross-language eigen dictionary, and a translation in a second language is assembled.

The invention discloses a machine translation method and a machine translation system. The machine translation method comprises the steps that a plurality of machine translation devices are respectively used for translating the original text of a source language into a target language to obtain a plurality of candidate translations; language model scores are respectively calculated for the candidate translations through a language model; the device scores, related to the candidate translations, given by the machine translation devices are respectively obtained; length scores are respectively calculated for the candidate translations based on the length of the original text and the length of the candidate translations; the total scores of the candidate translations are respectively calculated based on at least one of the language model scores, the device scores and the length scores; the candidate translation with the highest total score is selected as a machine translation result.

In one embodiment, the invention provides a method for translating a document in an input language into an output language comprising: a) for each document fragment for which a translation is readily available, translating said document fragment based on said readily available translation; and b) for each remaining untranslated fragment for which a translation is not readily available, translating said untranslated fragment based on a model-based machine translation technique. A translation is readily available if a search reveals at least one matching translation for the document fragment in a translation database.

The invention relates to an interactive machine translation method and an interactive machine translation system, for translating a source language text input to a computer into a target language text; the method comprises the following steps: reading a pre-stored machine translation model in a corresponding field according to a selection of users for the translation field; reading a to-be-translated text, and dividing the text into a series of sentences; performing operation according to the red to-be-translated sentence and the machine translation model to generate a search network of the to-be-translated sentence; when receiving words input by users, taking the input words as a search condition, searching with a decoding algorithm on the search network to obtain a translation result matched with the input words, wherein different user inputs corresponding to different translation results; if a result selection action is received from users, ending a translation process for the red to-be-translated sentence; if the result selection action is not received from users, continuously receiving word input of users until users select.

The invention provides an individualized machine translation system, a method and a translation model training method. The system comprises a first input module, a first training module, a universal translation model, a second input module, a second training module, a user translation model, a user identification module, a third input module and a translation module, wherein the universal translation model is used for describing the translation probability of source language sentences without translation preferences of users to target language sentences; the user translation model is used for describing the translation probability of source language sentences with translation preferences of the users to target language sentences; and the translation module is used for translating the information to be translated through the universal translation model and the matched user translation model so as to obtain a translation result. Aiming at identical information input by different users, the translation system can give translation results according to the translation preferences of the users.