Improving translation memory fuzzy matching by paraphrasing

Computer-assisted translation (CAT) tools have become the major language technology to support and facilitate the translation process. Those kind of programs store previously translated source texts and their equivalent target texts in a database and retrieve related segments during the translation of new texts. However, most of them are based on string or word edit distance, not allowing retrieving of matches that are similar. In this paper we present an innovative approach to match sentences having different words but the same meaning. We use NooJ to create paraphrases of Support Verb Constructions (SVC) of all source translation units to expand the fuzzy matching capabilities when searching in the translation memory (TM). Our first results for the EN-IT language pair show consistent and significant improvements in matching over state-of-the-art CAT systems, across different text domains.