Abstract: A translation between a source language and a target language is disclosed. The source language items are divided, with primary and secondary source language items or named entities being identified, where the primary and secondary source languages being different from each other and from the target language. The entities in the second source language are translated in a different way. For example, they may be copied into the target language, or translated in a special way that is based on their meaning, e.g, into a term that has a more descriptive meaning in the target language.

Abstract: Spoken translation system which detects both speech from the information and also detects meta information streams from the information. A first aspect produces an enriched training corpus of information for use in the machine translation. A second aspect uses two different extraction techniques, and combines them by lattice rescoring.

Abstract: A socially-cognizant translation system that takes social state between speaker and listener into account when making the translation. The translation may be more formal or less formal based on the social relationship between speaker and listener. Profanities in the source speech may be detected, and used to determine the social cognizance. Also, the source speech can be translated without the profanity in the target language, instead using the meaning of the profanity.

Abstract: An application (computer program, an embodiment can be a game) which requires translation as one of its metrics is used to help the user can learn a language while operating the system (in a game embodiment, playing the game). The interaction is carried out only in a foreign language, but the application also includes translation capability. A virtual buddy can be used to translate between the native language and the foreign language so that the user can translate information and eventually learn information about the language by the process of interacting with the system (in an embodiment playing the game).

Abstract: Forming and/or improving a language model based on data from a large collection of documents, such as web data. The collection of documents is queried using queries that are formed from the language model. The language model is subsequently improved using the information thus obtained. The improvement is used to improve the query. As data is received from the collection of documents, it is compared to a rejection model, that models what rejected documents typically look like. Any document that meets the test is then rejected. The documents that remain are characterized to determine whether they add information to the language model, whether they are relevant, and whether they should be independently rejected. Rejected documents are used to update the rejection model; accepted documents are used to update the language model. Each iteration improves the language model, and the documents may be analyzed again using the improved language model.

Abstract: A translation between a source language and a target language. The items are divided, with secondary source language items or named entities being identified. Those entities are translated in a different way. For example, they may be copied into the target language, or translated in a special way that is based on their meaning, e.g., into a term that has a more descriptive meaning in the target language.

Abstract: Spoken translation system which detects both speech from the information and also detects meta information streams from the information. A first aspect produces an enriched training corpus of information for use in the machine translation. A second aspect uses two different extraction techniques, and combines them by lattice rescoring.

Abstract: An application (computer program, an embodiment can be a game) which requires translation as one of its metrics is used to help the user can learn a language while operating the system (in a game embodiment, playing the game). The interaction is carried out only in a foreign language, but the application also includes translation capability. A virtual buddy can be used to translate between the native language and the foreign language so that the user can translate information and eventually learn information about the language by the process of interacting with the system (in an embodiment playing the game).

Abstract: A socially-cognizant translation system that takes social state between speaker and listener into account when making the translation. The translation may be more formal or less formal based on the social relationship between speaker and listener. Profanities in the source speech may be detected, and used to determine the social cognizance. Also, the source speech can be translated without the profanity in the target language, instead using the meaning of the profanity.

Abstract: Forming and/or improving a language model based on data from a large collection of documents, such as web data. The collection of documents is queried using queries that are formed from the language model. The language model is subsequently improved using the information thus obtained. The improvement is used to improve the query. As data is received from the collection of documents, it is compared to a rejection model, that models what rejected documents typically look like. Any document that meets the test is then rejected. The documents that remain are characterized to determine whether they add information to the language model, whether they are relevant, and whether they should be independently rejected. Rejected documents are used to update the rejection model; accepted documents are used to update the language model. Each iteration improves the language model, and the documents may be analyzed again using the improved language model.