Overcoming Poor Word Embeddings with Word Definitions

Christopher Malon


Abstract
Modern natural language understanding models depend on pretrained subword embeddings, but applications may need to reason about words that were never or rarely seen during pretraining. We show that examples that depend critically on a rarer word are more challenging for natural language inference models. Then we explore how a model could learn to use definitions, provided in natural text, to overcome this handicap. Our model’s understanding of a definition is usually weaker than a well-modeled word embedding, but it recovers most of the performance gap from using a completely untrained word.
Anthology ID:
2021.starsem-1.27
Volume:
Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics
Month:
August
Year:
2021
Address:
Online
Venues:
*SEM | ACL | IJCNLP
SIG:
SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
288–293
Language:
URL:
https://aclanthology.org/2021.starsem-1.27
DOI:
10.18653/v1/2021.starsem-1.27
Bibkey:
Cite (ACL):
Christopher Malon. 2021. Overcoming Poor Word Embeddings with Word Definitions. In Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics, pages 288–293, Online. Association for Computational Linguistics.
Cite (Informal):
Overcoming Poor Word Embeddings with Word Definitions (Malon, *SEM 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.starsem-1.27.pdf
Data
SNLI