Pre- and In-Parsing Models for Neural Empty Category Detection
Abstract
Motivated by the positive impact of empty category on syntactic parsing, we study neural models for pre- and in-parsing detection of empty category, which has not previously been investigated. We find several non-obvious facts: (a) BiLSTM can capture non-local contextual information which is essential for detecting empty categories, (b) even with a BiLSTM, syntactic information is still able to enhance the detection, and (c) automatic detection of empty categories improves parsing quality for overt words. Our neural ECD models outperform the prior state-of-the-art by significant margins.- Anthology ID:
- P18-1250
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2687–2696
- Language:
- URL:
- https://aclanthology.org/P18-1250
- DOI:
- 10.18653/v1/P18-1250
- Cite (ACL):
- Yufei Chen, Yuanyuan Zhao, Weiwei Sun, and Xiaojun Wan. 2018. Pre- and In-Parsing Models for Neural Empty Category Detection. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2687–2696, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Pre- and In-Parsing Models for Neural Empty Category Detection (Chen et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/P18-1250.pdf