1028 results about "Machine translation" patented technology

Disclosed is a semantic user interface system that allows text information to be tagged with machine-readable IDs that are associated with concepts for conveying information without any ambiguity or without being hampered by the limitations of human languages. Typically, a plurality of vocabularies are stored across a network, and each vocabulary includes a plurality of machine-readable IDs each corresponding to a concept and at least one keyword corresponding to each machine-readable ID. An input interface accepts text information, selects those machine-readable IDs whose keywords match up with the text information, and returns a list of candidates each corresponding to one of the selected machine-readable IDs and including a corresponding description. The machine-readable IDs can carry information in the form of concepts without any ambiguity as opposed to text information. This system can be applied to web and database searches, publishing messages to selected subscribers, interfacing of applications software, machine translations, etc.

A system and a method for phrase-based translation are disclosed. The method includes receiving source language text to be translated into target language text. One or more dynamic bi-phrases are generated, based on the source text and the application of one or more rules, which may be based on user descriptions. A dynamic feature value is associated with each of the dynamic bi-phrases. For a sentence of the source text, static bi-phrases are retrieved from a bi-phrase table, each of the static bi-phrases being associated with one or more values of static features. Any of the dynamic bi-phrases which each cover at least one word of the source text are also retrieved, which together form a set of active bi-phrases. Translation hypotheses are generated using active bi-phrases from the set and scored with a translation scoring model which takes into account the static and dynamic feature values of the bi-phrases used in the respective hypothesis. A translation, based on the hypothesis scores, is then output.

An authoring method includes generating an authoring interface configured for assisting a user to author a text string in a source language for translation to a target string in a target language. Initial source text entered by the user is received through the authoring interface. Source phrases are selected that each include at least one token of the initial source text as a prefix and at least one other token as a suffix. The source phrase selection is based on a translatability score and optionally on fluency and semantic relatedness scores. A set of candidate phrases is proposed for display on the authoring interface, each of the candidate phases being the suffix of a respective one of the selected source phrases. The user may select one of the candidate phrases, which is appended to the source text following its corresponding prefix, or may enter alternative text. The process may be repeated until the user is satisfied with the source text and the SMT model can then be used for its translation.

Systems, methods, and apparatus for accessing distributed models in automated machine processing, including using large language models in machine translation, speech recognition and other applications.

A method of improving the accuracy of the translation output of Statistical Machine Translation (SMT), while increasing the effectiveness of an ongoing professional human translation effort by correlating the ongoing professional human translation effort directly with the translation errors made by the system. Once the translation errors have been corrected by professional human translators and are re-input to the system, the SMT's training process may ensure that the same, and possibly similar, translation error(s) may not occur again.

In one embodiment, the invention provides a method for machine translation of a source document in an input language to a target document in an output language, comprising generating translation options corresponding to at least portions of each sentence in the input language; and selecting a translation option for the sentence based on statistics associated with the translation options.

A method and a system for making merging decisions for a translation are disclosed which are suited to use where the target language is a productive compounding one. The method includes outputting decisions on merging of pairs of words in a translated text string with a merging system. The merging system can include a set of stored heuristics and/or a merging model. In the case of heuristics, these can include a heuristic by which two consecutive words in the string are considered for merging if the first word of the two consecutive words is recognized as a compound modifier and their observed frequency f₁ as a closed compound word is larger than an observed frequency f₂ of the two consecutive words as a bigram. In the case of a merging model, it can be one that is trained on features associated with pairs of consecutive tokens of text strings in a training set and predetermined merging decisions for the pairs. A translation in the target language is output, based on the merging decisions for the translated text string.

A language recognition system, method and program product for recognizing language based input from computer users on a network of connected computers. Each computer includes at least one user based language model trained for a corresponding user for automatic speech recognition, handwriting recognition, machine translation, gesture recognition or other similar actions that require interpretation of user activities. Network computer users are clustered into classes of similar users according to user similarities such as, nationality, profession, sex, age, etc. User characteristics are collected by sensors and from databases and, then, distributed over the network during user activities. Language models with similarities among similar users on the network are identified. The language models include a language model domain, with similar language models being clustered according to their domains. Language models identified as similar are modified in response to user production activities. After modification of one language model, other identified similar language models are compared and adapted. Also, user data, including information about user activities and language model data, is transmitted over the network to other similar users. Language models are adapted only in response to similar user activities, when these activities are recorded and transmitted over the network. Language models are given a global context based on similar users that are connected together over the network.

A system and method for hybrid machine translation approach is based on a statistical transfer approach using statistical and linguistic features. The system and method may be used to translate from one language into another. The system may include at least one database, a rule based translation module, a statistical translation module and a hybrid machine translation engine. The database(s) store source and target text and rule based language models and statistical language models. The rule based translation module translates source text based on the rule based language models. The statistical translation module translates source text based on the statistical language models. A hybrid machine translation engine, having a maximum entropy algorithm, is coupled to the rule based translation module and the statistical translation module and is capable of translating source text into target text based on the rule based and statistical language models.

A method is disclosed for detecting topical changes and topic identification in texts in real time using likelihood ratio based methods. In accordance with the method, topic identification is achieved by evaluating text probabilities under each topic, and then selecting a new topic when one of those probabilities becomes significantly larger than the others. The method is usable to improve real time machine translation.

A method and apparatus for automating the acquisition, reconstruction, and generation of knowledgebases of associated ideas and using such knowledgebases in many applications including machine translation of human languages, search and retrieval of unstructured text, or other data, based on concept search, voice recognition, data compression, and artificial intelligence systems.

A quality-prediction engine predicts a trust level associated with translational accuracy of a machine-generated translation. Training a quality-prediction may include translating a document in a source language to a target language by executing a machine-translation engine stored in memory to obtain a machine-generated translation. The training may further include comparing the machine-generated translation with a human-generated translation of the document. The human-generated translation is in the target language. Additionally, the training may include generating a mapping between features of the machine-generated translation and features of the human-generated translation based on the comparison. The mapping may allow determination of trust levels associated with translational accuracy of future machine-generated translations that lack corresponding human-generated translations. Machine-generated translations may then be credibly provided by translating a document from a source language to a target language by executing a machine-translation engine stored in memory to obtain a machine-generated translation, predicting a trust level of the machine-generated translation by executing a quality-prediction engine stored in memory, and outputting the machine-generated translation and the trust level.

A translating apparatus includes a speech recognition unit, a machine translation unit, a speech synthesizing unit, and a dialog management unit. The speech recognition unit recognizes input speech, and supplies the result of the speech recognition to the machine translation unit. The machine translation unit translates the result of the speech recognition. When a Japanese-speaking user and an English-speaking user speak to each other, the dialog management unit stores a history of dialogs, and determines based on the dialog history and prestored knowledge whether the result of the translation by the machine translation unit is unnatural compared with the flow of the dialog. When the result of the translation is unnatural, the dialog management unit outputs, to the machine translation unit, information representing the unnatural result. This controls the machine translation unit to perform translation again.

A method and apparatus for stochastic finite-state machine translation is provided. The method may include receiving a speech input and translating the speech input in a source language into one or more symbols in a target language based on stochastic language model. Subsequently, all possible sequences of the translated symbols may be generated. One of the generated sequences may be selected based on a monolingual target language model.

This invention discloses one machine translation device, which comprises the following parts: sound information input unit to input first sound information; sound identification unit to identify first sound information and to convert it into text information; translation unit to translate text information into second one. This invention translation method comprises the following steps: sound information input unit to input first sound information; sound identification unit to identify first sound information and to convert it into text information; translation unit to translate text information into second one.

Named entity recognition is described, for example, to detect an instance of a named entity in a web page and classify the named entity as being an organization or other predefined class. In various examples, named entity recognition results are used to augment text from which the named entity was recognized; the augmentation may comprise information retrieval results about the named entity mention. In various embodiments, labeled training sentences in many different languages and for many different classes, are obtained to train machine learning components of a multi-lingual, multi-class, named entity recognition system. In examples, labeled training sentences are obtained from at least two sources, a first source using a multi-lingual or monolingual corpus of inter-linked documents and a second source using machine translation training data. In examples, labeled training sentences from the two sources are selectively sampled for training the named entity recognition system.

A machine translation method includes receiving source text in a first language and retrieving text fragments in a target language from a library of bi-fragments to generate a target hypothesis. Each bi-fragment includes a text fragment from the first language and a corresponding text fragment from the second language. Some of the bi-fragments are modeled as elastic bi-fragments where a gap between words is able to assume a variable size corresponding to a number of other words to occupy the gap. The target hypothesis is evaluated with a translation scoring function which scores the target hypothesis according to a plurality of feature functions, at least one of the feature functions comprising a gap size scoring feature which favors hypotheses with statistically more probable gap sizes over hypotheses with statically less probable gap sizes.