In this paper we investigate the automatic generation and evaluation of sentential paraphrases. We describe a method for generating sentential paraphrases by using a large aligned monolingual corpus of news headlines acquired automatically from Google News and a standard Phrase-Based Machine Translation (PBMT) framework. The output of this system is compared to a word substitution baseline. Human judges prefer the PBMT paraphrasing system over the word substitution system. We demonstrate that BLEU correlates well with human judgements provided that the generated paraphrased sentence is sufficiently different from the source sentence.
REF propose a phrase-based SMT framework for sentential paraphrase generation by using a large aligned monolingual corpus of news headlines.
Paraphrase Generation as Monolingual Translation: Data and Evaluation
Proceedings of the International Natural Language Generation Conference
Computer Science Psychology