Køpsala: Transition-Based Graph Parsing via Efficient Training and Effective Encoding
Daniel Hershcovich, Miryam de Lhoneux, Artur Kulmizev, Elham Pejhan, Joakim Nivre
Abstract
We present Køpsala, the Copenhagen-Uppsala system for the Enhanced Universal Dependencies Shared Task at IWPT 2020. Our system is a pipeline consisting of off-the-shelf models for everything but enhanced graph parsing, and for the latter, a transition-based graph parser adapted from Che et al. (2019). We train a single enhanced parser model per language, using gold sentence splitting and tokenization for training, and rely only on tokenized surface forms and multilingual BERT for encoding. While a bug introduced just before submission resulted in a severe drop in precision, its post-submission fix would bring us to 4th place in the official ranking, according to average ELAS. Our parser demonstrates that a unified pipeline is effective for both Meaning Representation Parsing and Enhanced Universal Dependencies.- Anthology ID:
- 2020.iwpt-1.25
- Volume:
- Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Venues:
- ACL | IWPT | WS
- SIG:
- SIGPARSE
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 236–244
- URL:
- https://www.aclweb.org/anthology/2020.iwpt-1.25
- DOI:
- PDF:
- https://www.aclweb.org/anthology/2020.iwpt-1.25.pdf
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