Combining Multiple Resources to Improve SMT-based Paraphrasing Model

Abstract

This paper proposes a novel method that ex-ploits multiple resources to improve statisti-cal machine translation (SMT) based para-phrasing. In detail, a phrasal paraphrase ta-ble and a feature function are derived from each resource, which are then combined in a log-linear SMTmodel for sentence-level para-phrase generation. Experimental results show that the SMT-based paraphrasing model can be enhanced using multiple resources. The phrase-level and sentence-level precision of the generated paraphrases are above 60 % and 55%, respectively. In addition, the contribu-tion of each resource is evaluated, which indi-cates that all the exploited resources are useful for generating paraphrases of high quality. 1