@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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<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.</abstract>
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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
Markdown (Informal)
[Contextualized context2vec](https://aclanthology.org/D19-5552) (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.