Køpsala: Transition-Based Graph Parsing via Efficient Training and Effective Encoding
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| Authors | Miryam de Lhoneux, Daniel Hershcovich, Artur Kulmizev, Joakim Nivre, Elham Pejhan |
| Journal/Conference Name | arXiv preprint |
| Paper Category | Artificial Intelligence |
| Paper Abstract | We present K{\o}psala, 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. |
| Date of publication | 2020 |
| Code Programming Language | Python |
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