Correlation Coefficients and Semantic Textual Similarity

Vitalii Zhelezniak, Aleksandar Savkov, April Shen, Nils Hammerla


Abstract
A large body of research into semantic textual similarity has focused on constructing state-of-the-art embeddings using sophisticated modelling, careful choice of learning signals and many clever tricks. By contrast, little attention has been devoted to similarity measures between these embeddings, with cosine similarity being used unquestionably in the majority of cases. In this work, we illustrate that for all common word vectors, cosine similarity is essentially equivalent to the Pearson correlation coefficient, which provides some justification for its use. We thoroughly characterise cases where Pearson correlation (and thus cosine similarity) is unfit as similarity measure. Importantly, we show that Pearson correlation is appropriate for some word vectors but not others. When it is not appropriate, we illustrate how common non-parametric rank correlation coefficients can be used instead to significantly improve performance. We support our analysis with a series of evaluations on word-level and sentence-level semantic textual similarity benchmarks. On the latter, we show that even the simplest averaged word vectors compared by rank correlation easily rival the strongest deep representations compared by cosine similarity.
Anthology ID:
N19-1100
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
951–962
Language:
URL:
https://aclanthology.org/N19-1100
DOI:
10.18653/v1/N19-1100
Bibkey:
Cite (ACL):
Vitalii Zhelezniak, Aleksandar Savkov, April Shen, and Nils Hammerla. 2019. Correlation Coefficients and Semantic Textual Similarity. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 951–962, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Correlation Coefficients and Semantic Textual Similarity (Zhelezniak et al., NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-1100.pdf
Code
 Babylonpartners/corrsim