K{\o}psala: Transition-Based Graph Parsing via Efficient Training and Effective Encoding

• URL: http://arxiv.org/abs/2005.12094v2
• Date: Tue, 2 Jun 2020 11:45:56 GMT
• Title: K{\o}psala: Transition-Based Graph Parsing via Efficient Training and Effective Encoding
• Authors: Daniel Hershcovich, Miryam de Lhoneux, Artur Kulmizev, Elham Pejhan, Joakim Nivre
• Abstract summary: 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 parsing, and for the latter, a transition-based graphencies adapted from Che et al. Our demonstrates that a unified pipeline is effective for both Representation Parsing and Enhanced Universal Dependencies, according to average ELAS.
• Score: 13.490365811869719
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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