Pre- and In-Parsing Models for Neural Empty Category Detection

Yufei Chen, Yuanyuan Zhao, Weiwei Sun, Xiaojun Wan


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
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/P18-1250.pdf
Software:
 P18-1250.Software.zip
Poster:
 P18-1250.Poster.pdf