Køpsala:Transition-Based Graph Parsing via Efficient Training and Effective Encoding
| Autor: | Joakim Nivre, Daniel Hershcovich, Artur Kulmizev, Elham Pejhan, Miryam de Lhoneux |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
050101 languages & linguistics
Parsing Computer science business.industry 05 social sciences Graph parsing 02 engineering and technology computer.software_genre 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Artificial intelligence business computer Natural language processing Sentence Universal dependencies |
| Zdroj: | Hershcovich, D, De Lhoneux, M, Kulmizev, A, Pejhan, E & Nivre, J 2020, Køpsala : Transition-Based Graph Parsing via Efficient Training and Effective Encoding . in Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies . Association for Computational Linguistics, pp. 236-244, 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task, Virtual Meeting, 09/07/2020 . https://doi.org/10.18653/v1/2020.iwpt-1.25 IWPT 2020 |
| DOI: | 10.18653/v1/2020.iwpt-1.25 |
| Popis: | We present Kopsala, 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. |
| Databáze: | OpenAIRE |
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