Køpsala: Transition-Based Graph Parsing via Efficient Training and Effective Encoding
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Documents
- OA-Køpsala
Final published version, 294 KB, PDF document
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.
Original language | English |
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Title of host publication | Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies |
Publisher | Association for Computational Linguistics |
Publication date | 2020 |
Pages | 236-244 |
DOIs | |
Publication status | Published - 2020 |
Event | 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task - Virtual Meeting Duration: 9 Jul 2020 → … |
Conference
Conference | 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task |
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By | Virtual Meeting |
Periode | 09/07/2020 → … |
ID: 254669146