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

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Dokumenter

  • OA-Køpsala

    Forlagets udgivne version, 294 KB, PDF-dokument

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.
OriginalsprogEngelsk
TitelProceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies
ForlagAssociation for Computational Linguistics
Publikationsdato2020
Sider236-244
DOI
StatusUdgivet - 2020
Begivenhed16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task - Virtual Meeting
Varighed: 9 jul. 2020 → …

Konference

Konference16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task
ByVirtual Meeting
Periode09/07/2020 → …

ID: 254669146