Abstract
We explore the application of a Deep Structured Similarity Model (DSSM) to ranking in lexical simplification. Our results show that the DSSM can effectively capture fine-grained features to perform semantic matching when ranking substitution candidates, outperforming the state-of-the-art on two standard datasets used for the task.- Anthology ID:
- I17-2073
- Volume:
- Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
- Month:
- November
- Year:
- 2017
- Address:
- Taipei, Taiwan
- Editors:
- Greg Kondrak, Taro Watanabe
- Venue:
- IJCNLP
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 430–435
- Language:
- URL:
- https://aclanthology.org/I17-2073
- DOI:
- Cite (ACL):
- Lis Pereira, Xiaodong Liu, and John Lee. 2017. Lexical Simplification with the Deep Structured Similarity Model. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 430–435, Taipei, Taiwan. Asian Federation of Natural Language Processing.
- Cite (Informal):
- Lexical Simplification with the Deep Structured Similarity Model (Pereira et al., IJCNLP 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/I17-2073.pdf