@inproceedings{chang-chen-2019-word,
title = "What Does This Word Mean? Explaining Contextualized Embeddings with Natural Language Definition",
author = "Chang, Ting-Yun and
Chen, Yun-Nung",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1627",
doi = "10.18653/v1/D19-1627",
pages = "6064--6070",
abstract = "Contextualized word embeddings have boosted many NLP tasks compared with traditional static word embeddings. However, the word with a specific sense may have different contextualized embeddings due to its various contexts. To further investigate what contextualized word embeddings capture, this paper analyzes whether they can indicate the corresponding sense definitions and proposes a general framework that is capable of explaining word meanings given contextualized word embeddings for better interpretation. The experiments show that both ELMo and BERT embeddings can be well interpreted via a readable textual form, and the findings may benefit the research community for a better understanding of what the embeddings capture.",
}
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%0 Conference Proceedings
%T What Does This Word Mean? Explaining Contextualized Embeddings with Natural Language Definition
%A Chang, Ting-Yun
%A Chen, Yun-Nung
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F chang-chen-2019-word
%X Contextualized word embeddings have boosted many NLP tasks compared with traditional static word embeddings. However, the word with a specific sense may have different contextualized embeddings due to its various contexts. To further investigate what contextualized word embeddings capture, this paper analyzes whether they can indicate the corresponding sense definitions and proposes a general framework that is capable of explaining word meanings given contextualized word embeddings for better interpretation. The experiments show that both ELMo and BERT embeddings can be well interpreted via a readable textual form, and the findings may benefit the research community for a better understanding of what the embeddings capture.
%R 10.18653/v1/D19-1627
%U https://aclanthology.org/D19-1627
%U https://doi.org/10.18653/v1/D19-1627
%P 6064-6070
Markdown (Informal)
[What Does This Word Mean? Explaining Contextualized Embeddings with Natural Language Definition](https://aclanthology.org/D19-1627) (Chang & Chen, EMNLP-IJCNLP 2019)
ACL