A translation apparatus includes a translation unit which translates content of a document into a different language, a history creating unit which, in translation of the content from a first language into a second language, creates history information including a correspondence between original text in the first language and translated text in the second language, an extraction unit which, in translation of the content from the second language into another language, if content (present content) of the document in the second language is present in the history information, extracts content (absent content) that is not present in the history information, and a combining unit which combines a translation result obtained by translating the present content from the second language into the other language, with a replacement result obtained by replacing the absent content from the second language to the other language based on the history information.

A computerized VoIP system which provides a computerized service for facilitating face-to-face and/or telephone meetings, in real time, between persons lacking a common language or having language barriers such as accents and dialects e.g. by utilizing or generating a networked worldwide community of Simultaneous Interpreters, using e.g. POTS (Plain Old Telephone Service), Smart Phone or any mobile phone. A platform may thereby be provided for professional simultaneous interpreters and business/private people, where interpreters from all over the world may translate any face-to-face meeting or telephone call between business people in any combination of languages, in real time.

Exemplary embodiments relate to techniques for selecting translators willing to provide high-quality translations for a cause, organization, or individual. Users having a high level of engagement with the cause, organization, or individual may be identified as translator candidates. For example, the user may actively engage with the organization or individual on social media, or may be interested in the topics discussed in the source document. The translators may be evaluated based on the quality of their previous translations and their level of engagement/interest. The translator candidates may be directly connected with the originator of the request to translate the document. Because exemplary embodiments select highly engaged users to translate the source document, the resulting translation is likely to be of higher quality, and produced at a lower cost, than a translation by a non-engaged user, and user participation and awareness of a cause, organization, or individual may be increased.

Exemplary embodiments relate to detecting, removing, and/or replacing objectionable words and phrases in a machine-generated translation. A classifier identifies translations containing target words or phrases. The classifier may be applied to the output translation to remove target words and phrases from the translation, or to prevent target words and phrases from being automatically presented. Further, the classifier may be applied to a translation model to prevent the target words and phrases from appearing in the output translation. Still further, the classifier may be applied to training data so that the translation model is not trained using the target words of phrases. The classifier may remove target words or phrases only when the target words or phrases appear in the output translation but not the source language input data. The classifier may be provided as a standalone service, or may be employed in the context of a machine translation system.

A system and method for electronic document translation filtering includes an input that receives electronic document data and instruction data corresponding to at least one document processing operation to be performed on the electronic document data. A computer includes a processor and associated memory identifies a language associated with the electronic document data and receives a translation instruction corresponding to at least one target language. The computer applies language filter data specified by the translation instruction to the electronic document data to generate translated electronic document data. A translated electronic document resultant from application of the language filter data to the electronic document data is stored and an output communicates the translated electronic document data to a document processing engine.

Exemplary embodiments relate to techniques for improving a machine translation system. The machine translation system may include one or more models for generating a translation. The system may generate multiple candidate translations, and may present the candidate translations to different groups of users, such as users of a social network. User engagement with the different candidate translations may be measured, and the system may determine which of the candidate translations was most favored by the users. For example, in the context of a social network, the number of times that the translation is liked or shared, or the number of comments associated with the translation, may be used to determine user engagement with the translation. The models of the machine translation system may be modified to favor the most-favored candidate translation. The translation system may repeat this process to continue to tune the models in a feedback loop.

Exemplary embodiments relate to techniques for improving machine translation systems. The machine translation system may apply one or more models for translating material from a source language into a destination language. The models are initially trained using training data. According to exemplary embodiments, supplemental training data is used to train the models, where the supplemental training data uses in-domain material to improve the quality of output translations. In-domain data may include data that relates to the same or similar topics as those expected to be encountered in a translation of material from the source language into the destination language. In-domain data may include material previously translated from the source language into the destination language, material similar to previous translations, and destination language material that has previously been the subject of a request for translation into the source language.

Exemplary embodiments relate to detecting, removing, and/or replacing objectionable words and phrases in a machine-generated translation. A classifier identifies translations containing target words or phrases. The classifier may be applied to the output translation to remove target words and phrases from the translation, or to prevent target words and phrases from being automatically presented. Further, the classifier may be applied to a translation model to prevent the target words and phrases from appearing in the output translation. Still further, the classifier may be applied to training data so that the translation model is not trained using the target words of phrases. The classifier may remove target words or phrases only when the target words or phrases appear in the output translation but not the source language input data. The classifier may be provided as a standalone service, or may be employed in the context of a machine translation system.

Exemplary embodiments provide techniques for evaluating when words or phrases of a translation were generated with a low degree of confidence, and conveying this information when the translation is presented. For example, if a source language word is encountered in source material for translation, but the source language word was only encountered a few times (or not at all) in the training data used to train the translation system, then the resulting translation may be flagged as being of low confidence. Other situations, such as the generation of two equally-likely translations, or translation system model disagreement, may also indicate a questionable translation. When the translation is displayed, questionable words and phrases may be flagged, and possible alternative translations may be presented. If one of the alternatives is selected, this information may be used to update the translation system's models in order to improve translation quality in the future.

Software translation quality and efficiency are improved by providing user interface (UI) context for translators. Unicode symbols are used to uniquely tag user-visible strings from the source code and into resource files. Those strings include titles, product names, error messages, strings in images and any other text that may be present on the user interface. Once the ‘pseudo’ resource files are integrated into a build, automation is run to gather screenshots of the application. Image recognition is then used to link screenshots of the UI in which a resource file string appears, such that screenshots will be brought forward and displayed to the translator when working on translating the user-visible strings of the software being localized.