Abstract
We present SParse, our Graph-Based Parsing model submitted for the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies (Zeman et al., 2018). Our model extends the state-of-the-art biaffine parser (Dozat and Manning, 2016) with a structural meta-learning module, SMeta, that combines local and global label predictions. Our parser has been trained and run on Universal Dependencies datasets (Nivre et al., 2016, 2018) and has 87.48% LAS, 78.63% MLAS, 78.69% BLEX and 81.76% CLAS (Nivre and Fang, 2017) score on the Italian-ISDT dataset and has 72.78% LAS, 59.10% MLAS, 61.38% BLEX and 61.72% CLAS score on the Japanese-GSD dataset in our official submission. All other corpora are evaluated after the submission deadline, for whom we present our unofficial test results.- Anthology ID:
- K18-2022
- 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:
- 216–222
- Language:
- URL:
- https://aclanthology.org/K18-2022
- DOI:
- 10.18653/v1/K18-2022
- Cite (ACL):
- Berkay Önder, Can Gümeli, and Deniz Yuret. 2018. SParse: Koç University Graph-Based Parsing System for the CoNLL 2018 Shared Task. In Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 216–222, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- SParse: Koç University Graph-Based Parsing System for the CoNLL 2018 Shared Task (Önder et al., CoNLL 2018)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/K18-2022.pdf
- Data
- Universal Dependencies