Contextualized context2vec

Kazuki Ashihara; Tomoyuki Kajiwara; Yuki Arase†; Satoru Uchida

doi:10.18653/v1/D19-5552

Contextualized context2vec

Kazuki Ashihara, Tomoyuki Kajiwara, Yuki Arase, Satoru Uchida

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Abstract

Lexical substitution ranks substitution candidates from the viewpoint of paraphrasability for a target word in a given sentence. There are two major approaches for lexical substitution: (1) generating contextualized word embeddings by assigning multiple embeddings to one word and (2) generating context embeddings using the sentence. Herein we propose a method that combines these two approaches to contextualize word embeddings for lexical substitution. Experiments demonstrate that our method outperforms the current state-of-the-art method. We also create CEFR-LP, a new evaluation dataset for the lexical substitution task. It has a wider coverage of substitution candidates than previous datasets and assigns English proficiency levels to all target words and substitution candidates.

Anthology ID:: D19-5552
Volume:: Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
Month:: November
Year:: 2019
Address:: Hong Kong, China
Editors:: Wei Xu, Alan Ritter, Tim Baldwin, Afshin Rahimi
Venue:: WNUT
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 397–406
Language:
URL:: https://aclanthology.org/D19-5552/
DOI:: 10.18653/v1/D19-5552
Bibkey:
Cite (ACL):: Kazuki Ashihara, Tomoyuki Kajiwara, Yuki Arase, and Satoru Uchida. 2019. Contextualized context2vec. In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pages 397–406, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):: Contextualized context2vec (Ashihara et al., WNUT 2019)
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PDF:: https://aclanthology.org/D19-5552.pdf

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@inproceedings{ashihara-etal-2019-contextualized,
    title = "Contextualized context2vec",
    author = "Ashihara, Kazuki  and
      Kajiwara, Tomoyuki  and
      Arase, Yuki  and
      Uchida, Satoru",
    editor = "Xu, Wei  and
      Ritter, Alan  and
      Baldwin, Tim  and
      Rahimi, Afshin",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D19-5552/",
    doi = "10.18653/v1/D19-5552",
    pages = "397--406",
    abstract = "Lexical substitution ranks substitution candidates from the viewpoint of paraphrasability for a target word in a given sentence. There are two major approaches for lexical substitution: (1) generating contextualized word embeddings by assigning multiple embeddings to one word and (2) generating context embeddings using the sentence. Herein we propose a method that combines these two approaches to contextualize word embeddings for lexical substitution. Experiments demonstrate that our method outperforms the current state-of-the-art method. We also create CEFR-LP, a new evaluation dataset for the lexical substitution task. It has a wider coverage of substitution candidates than previous datasets and assigns English proficiency levels to all target words and substitution candidates."
}

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%0 Conference Proceedings
%T Contextualized context2vec
%A Ashihara, Kazuki
%A Kajiwara, Tomoyuki
%A Arase, Yuki
%A Uchida, Satoru
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F ashihara-etal-2019-contextualized
%X Lexical substitution ranks substitution candidates from the viewpoint of paraphrasability for a target word in a given sentence. There are two major approaches for lexical substitution: (1) generating contextualized word embeddings by assigning multiple embeddings to one word and (2) generating context embeddings using the sentence. Herein we propose a method that combines these two approaches to contextualize word embeddings for lexical substitution. Experiments demonstrate that our method outperforms the current state-of-the-art method. We also create CEFR-LP, a new evaluation dataset for the lexical substitution task. It has a wider coverage of substitution candidates than previous datasets and assigns English proficiency levels to all target words and substitution candidates.
%R 10.18653/v1/D19-5552
%U https://aclanthology.org/D19-5552/
%U https://doi.org/10.18653/v1/D19-5552
%P 397-406

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Markdown (Informal)

[Contextualized context2vec](https://aclanthology.org/D19-5552/) (Ashihara et al., WNUT 2019)

Contextualized context2vec (Ashihara et al., WNUT 2019)

ACL

Kazuki Ashihara, Tomoyuki Kajiwara, Yuki Arase, and Satoru Uchida. 2019. Contextualized context2vec. In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pages 397–406, Hong Kong, China. Association for Computational Linguistics.