@inproceedings{malon-2021-overcoming,
title = "Overcoming Poor Word Embeddings with Word Definitions",
author = "Malon, Christopher",
editor = "Ku, Lun-Wei and
Nastase, Vivi and
Vuli{\'c}, Ivan",
booktitle = "Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.starsem-1.27/",
doi = "10.18653/v1/2021.starsem-1.27",
pages = "288--293",
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."
}
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%0 Conference Proceedings
%T Overcoming Poor Word Embeddings with Word Definitions
%A Malon, Christopher
%Y Ku, Lun-Wei
%Y Nastase, Vivi
%Y Vulić, Ivan
%S Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F malon-2021-overcoming
%X 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.
%R 10.18653/v1/2021.starsem-1.27
%U https://aclanthology.org/2021.starsem-1.27/
%U https://doi.org/10.18653/v1/2021.starsem-1.27
%P 288-293
Markdown (Informal)
[Overcoming Poor Word Embeddings with Word Definitions](https://aclanthology.org/2021.starsem-1.27/) (Malon, *SEM 2021)
ACL