Xerox Corporation

Christophe Servan

Marc Dymetman

Claude Roux

A method for generating a natural language response to a customer inquiry includes parsing sentences in a corpus of natural language dialogs between a respective customer and an agent to extract dependencies. Each parsed sentence is represented by a dependency graph, based on the extracted dependencies. Dependency templates are generated, at least some of which are each generated from two or more of the extracted dependency graphs (e.g., using variables which each represent a group of words referring to a same topic) and are stored in a knowledge base. In response to the customer inquiry, one of the stored dependency templates is identified and a natural language response to the inquiry is generated, using the identified dependency template. The natural language response is generated based on words extracted from the natural language dialogs that are linked, in memory, to the identified dependency template.

System and method for response generation using linguistic information

A method for terminological adaptation includes receiving a vocabulary pair including source and target language terms. Each term is in a class which includes a set of sequences. Contextual phrase pairs are extracted from a bilingual training corpus, each including source and target phrases. The phrases each include a sequence of the same class as the respective source and target terms as well as some associated context. Templates are generated, based on the contextual phrase pairs. In each template the source and target sequences of a contextual phrase pair are replaced with respective placeholders, each denoting the respective class of the sequence. Candidate phrase pairs are generated from these templates. In each candidate phrase pair, the placeholders of one of the templates are replaced with respective terms of a vocabulary pair of the same class. Some candidate phrase pairs are incorporated into a phrase table of a machine translation system.

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Crolles, France

Christophe Servan