Style-transfer and Paraphrase: Looking for a Sensible Semantic Similarity Metric


  • Ivan P. Yamshchikov Max Planck Institute for Mathematics in the Sciences
  • Viacheslav Shibaev Ural Federal University
  • Nikolay Khlebnikov Ural Federal University
  • Alexey Tikhonov Yandex



Lexical & Frame Semantics, Semantic Parsing, General


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.




How to Cite

Yamshchikov, I. P., Shibaev, V., Khlebnikov, N., & Tikhonov, A. (2021). Style-transfer and Paraphrase: Looking for a Sensible Semantic Similarity Metric. Proceedings of the AAAI Conference on Artificial Intelligence, 35(16), 14213-14220.



AAAI Technical Track on Speech and Natural Language Processing III