Abstract
This paper presents our experiments with applying TUPA to the CoNLL 2018 UD shared task. TUPA is a general neural transition-based DAG parser, which we use to present the first experiments on recovering enhanced dependencies as part of the general parsing task. TUPA was designed for parsing UCCA, a cross-linguistic semantic annotation scheme, exhibiting reentrancy, discontinuity and non-terminal nodes. By converting UD trees and graphs to a UCCA-like DAG format, we train TUPA almost without modification on the UD parsing task. The generic nature of our approach lends itself naturally to multitask learning.- Anthology ID:
- K18-2010
- Volume:
- Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
- Month:
- October
- Year:
- 2018
- Address:
- Brussels, Belgium
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 103–112
- Language:
- URL:
- https://aclanthology.org/K18-2010
- DOI:
- 10.18653/v1/K18-2010
- Cite (ACL):
- Daniel Hershcovich, Omri Abend, and Ari Rappoport. 2018. Universal Dependency Parsing with a General Transition-Based DAG Parser. In Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 103–112, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Universal Dependency Parsing with a General Transition-Based DAG Parser (Hershcovich et al., CoNLL 2018)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/K18-2010.pdf
- Code
- CoNLL-UD-2018/HUJI
- Data
- Universal Dependencies