AI summary

This paper introduces the Microsoft Research Paraphrase Corpus (MSRP), a dataset of 5801 hand-labeled sentence pairs, created using heuristic extraction and an SVM classifier, to address the lack of large-scale, publicly available paraphrase corpora.

Abstract

An obstacle to research in automatic paraphrase identification and generation is the lack of large-scale, publicly-available labeled corpora of sentential paraphrases. This paper describes the creation of the recently-released Microsoft Research Paraphrase Corpus, which contains 5801 sentence pairs, each hand-labeled with a binary judgment as to whether the pair constitutes a paraphrase. The corpus was created using heuristic extraction techniques in conjunction with an SVM-based classifier to select likely sentence-level paraphrases from a large corpus of topic-clustered news data. These pairs were then submitted to human judges, who confirmed that 67% were in fact semantically equivalent. In addition to describing the corpus itself, we explore a number of issues that arose in defining guidelines for the human raters.