Word2Box: Capturing Set-Theoretic Semantics of Words using Box Embeddings

Shib Dasgupta, Michael Boratko, Siddhartha Mishra, Shriya Atmakuri, Dhruvesh Patel, Xiang Li, Andrew McCallum


Abstract
Learning representations of words in a continuous space is perhaps the most fundamental task in NLP, however words interact in ways much richer than vector dot product similarity can provide. Many relationships between words can be expressed set-theoretically, for example, adjective-noun compounds (eg. “red cars”⊆“cars”) and homographs (eg. “tongue”∩“body” should be similar to “mouth”, while “tongue”∩“language” should be similar to “dialect”) have natural set-theoretic interpretations. Box embeddings are a novel region-based representation which provide the capability to perform these set-theoretic operations. In this work, we provide a fuzzy-set interpretation of box embeddings, and learn box representations of words using a set-theoretic training objective. We demonstrate improved performance on various word similarity tasks, particularly on less common words, and perform a quantitative and qualitative analysis exploring the additional unique expressivity provided by Word2Box.
Anthology ID:
2022.acl-long.161
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2263–2276
Language:
URL:
https://aclanthology.org/2022.acl-long.161
DOI:
10.18653/v1/2022.acl-long.161
Bibkey:
Cite (ACL):
Shib Dasgupta, Michael Boratko, Siddhartha Mishra, Shriya Atmakuri, Dhruvesh Patel, Xiang Li, and Andrew McCallum. 2022. Word2Box: Capturing Set-Theoretic Semantics of Words using Box Embeddings. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2263–2276, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Word2Box: Capturing Set-Theoretic Semantics of Words using Box Embeddings (Dasgupta et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.161.pdf
Software:
 2022.acl-long.161.software.zip
Code
 iesl/word2box