Looking up phrase rephrasings via a pivot language

Rephrasing text spans is a common task when revising a text. However, traditional dictionaries often cannot provide direct assistance to writers in performing this task. In this article, we describe an approach to obtain a monolingual phrase lexicon using techniques used in Statistical Machine Translation. A part to be rephrased is first translated into a pivot language, and then translated back into the original language. Models for assessing fluency, meaning preservation and lexical divergence are used to rank possible rephrasings, and their relative weight can be tuned by the user so as to better address her needs. An evaluation shows that these models can be used successfully to select rephrasings that are likely to be useful to a writer.