End-to-End Graph-Based TAG Parsing with Neural Networks
Jungo Kasai, Robert Frank, Pauli Xu, William Merrill, Owen Rambow
Abstract
We present a graph-based Tree Adjoining Grammar (TAG) parser that uses BiLSTMs, highway connections, and character-level CNNs. Our best end-to-end parser, which jointly performs supertagging, POS tagging, and parsing, outperforms the previously reported best results by more than 2.2 LAS and UAS points. The graph-based parsing architecture allows for global inference and rich feature representations for TAG parsing, alleviating the fundamental trade-off between transition-based and graph-based parsing systems. We also demonstrate that the proposed parser achieves state-of-the-art performance in the downstream tasks of Parsing Evaluation using Textual Entailments (PETE) and Unbounded Dependency Recovery. This provides further support for the claim that TAG is a viable formalism for problems that require rich structural analysis of sentences.- Anthology ID:
- N18-1107
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marilyn Walker, Heng Ji, Amanda Stent
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1181–1194
- Language:
- URL:
- https://aclanthology.org/N18-1107
- DOI:
- 10.18653/v1/N18-1107
- Cite (ACL):
- Jungo Kasai, Robert Frank, Pauli Xu, William Merrill, and Owen Rambow. 2018. End-to-End Graph-Based TAG Parsing with Neural Networks. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1181–1194, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- End-to-End Graph-Based TAG Parsing with Neural Networks (Kasai et al., NAACL 2018)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/N18-1107.pdf
- Code
- jungokasai/graph_parser
- Data
- Penn Treebank