Køpsala: Transition-Based Graph Parsing via Efficient Training and Effective Encoding

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  • 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 languageEnglish
Title of host publicationProceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies
PublisherAssociation for Computational Linguistics
Publication date2020
Pages236-244
DOIs
Publication statusPublished - 2020
Event16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task - Virtual Meeting
Duration: 9 Jul 2020 → …

Conference

Conference16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task
ByVirtual Meeting
Periode09/07/2020 → …

ID: 254669146