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
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/D19-5552.pdf