Collecting Highly Parallel Data for Paraphrase Evaluation
David J. Chen, William B. Dolan · 2011
A lack of standard datasets and evaluation metrics has prevented the field of paraphras-ing from making the kind of rapid progress enjoyed by the machine translation commu-nity over the last 15 years. We address both problems by presenting a novel data collection framework that produces highly parallel text data relatively inexpensively and on a large scale. The highly parallel nature of this data allows us to use simple n-gram comparisons to measure both the semantic adequacy and lex-ical dissimilarity of paraphrase candidates. In addition to being simple and efficient to com-pute, experiments show that these metrics cor-relate highly with human judgments. 1