风格转换与释义：寻找一个合理的语义相似度度量
Style-transfer and Paraphrase: Looking for a Sensible Semantic Similarity Metric
摘要：
风格转换、释义和机器翻译等自然语言处理任务的快速发展经常需要使用语义相似性度量。近年来，人们提出了许多度量两个短文本语义相似度的方法。本文对十几种这样的方法进行了综合分析。使用一个新的数据集的14000句对人类标记根据其语义相似性，我们证明，没有一个广泛使用的指标在文献中是足够接近人类的判断在这些任务。最近提出的一些指标提供了可比的结果，但词移动距离被证明是最合理的解决方案，以衡量语义相似性在重构文本的时刻。
英文摘要：
The rapid development of such natural language processing tasks as style transfer, paraphrase, and machine translation often calls for the use of semantic similarity metrics. In recent years a lot of methods to measure the semantic similarity of two short texts were developed. This paper provides a comprehensive analysis for more than a dozen of such methods. Using a new dataset of fourteen thousand sentence pairs human-labeled according to their semantic similarity, we demonstrate that none of the metrics widely used in the literature is close enough to human judgment in these tasks. A number of recently proposed metrics provide comparable results, yet Word Mover Distance is shown to be the most reasonable solution to measure semantic similarity in reformulated texts at the moment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
