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: 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 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: 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 method is provided for identifying phrase alignment pairs between a source sentence and a target sentence. Boundaries for a phrase in the source sentence are identified by requiring that a source word be aligned with at least one target word in a target sentence in order to form a boundary for the source phrase. Boundaries for a phrase in the target sentence are identified based on alignments between words in the source phrase and words in the target sentence. The words in the target phrase are examined to determine if any of the words are aligned with source words outside of the source phrase. If they are not aligned with source words outside of the source phrase, the source phrase and target phrase are determined to form an alignment pair and are stored as a phrase alignment pair.

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.