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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10426921" target="_blank" >RIV/00216208:11320/20:10426921 - isvavai.cz</a>

Result on the web

<a href="https://www.aclweb.org/anthology/2020.iwpt-1.25" target="_blank" >https://www.aclweb.org/anthology/2020.iwpt-1.25</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

O - Miscellaneous

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů