ETS: Discriminative Edit Models for Paraphrase Scoring
Michael Heilman, Nitin Madnani · 2012
Many problems in natural language processing can be viewed as variations of the task of measuring the semantic textual similarity between short texts. However, many systems that address these tasks focus on a single task and may or may not generalize well. In this work, we extend an existing machine translation metric, TERp (Snover et al., 2009a), by adding support for more detailed feature types and by implementing a discriminative learning algorithm. These additions facilitate applications of our system, called PERP, to similarity tasks other than machine translation evaluation, such as paraphrase recognition. In the SemEval 2012 Semantic Textual Similarity task, PERP performed competitively, particularly at the two surprise subtasks revealed shortly before the submission deadline. 1