Abstract: A proactive execution system receives messages or other information from a plurality of different information channels. The proactive execution system automatically identifies messages that include a request for a user to perform a task. The proactive execution system then automatically generates a plan for executing that task and calls a plan execution system, with the plan, to perform the task. The proactive execution system receives a result from the plan execution system and generates an output indicative of that result, for access by the user.

Abstract: Automated language translation often involves language translation resources of significant size (e.g., 50-gigabyte phrase tables) and significant computational power exceeding the capabilities of many mobile devices. Remotely accessible servers capable of near-realtime, automated translation may be inaccessible or prohibitively costly while traveling abroad. Presented herein are adaptations of language translation techniques for offline mobile devices involving reducing the size and raising the efficiency of the language modeling resources. A word index may be provided that stores respective string representations of the words of a language, and maps respective words to a location (e.g., address or offset) of respective word representations within the word index. Language translation resources (e.g., phrase tables) may then specify logical relationships using the word index addresses of the involved words, rather than the string equivalents.

Abstract: Automated language translation often involves language translation resources of significant size (e.g., 50-gigabyte phrase tables) and significant computational power exceeding the capabilities of many mobile devices. Remotely accessible servers capable of near-realtime, automated translation may be inaccessible or prohibitively costly while traveling abroad. Presented herein are adaptations of language translation techniques for offline mobile devices involving reducing the size and raising the efficiency of the language modeling resources. A word index may be provided that stores respective string representations of the words of a language, and maps respective words to a location (e.g., address or offset) of respective word representations within the word index. Language translation resources (e.g., phrase tables) may then specify logical relationships using the word index addresses of the involved words, rather than the string equivalents.

Abstract: A method of translation includes uploading a source text portion to a back end processor. The back end processor identifies a subset of translation knowledge associated with the source text portion. The back end processor downloads the subset to a front end processor. A translation engine runs on the front end processor. The translation engine generates a translation of the source text portion as a function of the subset.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method of decoding an input semantic structure to generate an output semantic structure. A set of transfer mappings are provided. A score is calculated for at least one transfer mapping in the set of transfer mappings using a statistical model. At least one transfer mapping is selected based on the score and used to construct the output semantic structure.

Abstract: The present invention obtains a set of text segments from a cluster of different articles written about a common event. The set of text segments is then subjected to textual alignment techniques to identify paraphrases from the text segments in the text. The invention can also be used to generate paraphrases.

Abstract: 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.