@inproceedings{hershcovich-etal-2020-kopsala,
title = "{K}{\o}psala: Transition-Based Graph Parsing via Efficient Training and Effective Encoding",
author = "Hershcovich, Daniel and
de Lhoneux, Miryam and
Kulmizev, Artur and
Pejhan, Elham and
Nivre, Joakim",
editor = "Bouma, Gosse and
Matsumoto, Yuji and
Oepen, Stephan and
Sagae, Kenji and
Seddah, Djam{\'e} and
Sun, Weiwei and
S{\o}gaard, Anders and
Tsarfaty, Reut and
Zeman, Dan",
booktitle = "Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.iwpt-1.25/",
doi = "10.18653/v1/2020.iwpt-1.25",
pages = "236--244",
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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%0 Conference Proceedings
%T Køpsala: Transition-Based Graph Parsing via Efficient Training and Effective Encoding
%A Hershcovich, Daniel
%A de Lhoneux, Miryam
%A Kulmizev, Artur
%A Pejhan, Elham
%A Nivre, Joakim
%Y Bouma, Gosse
%Y Matsumoto, Yuji
%Y Oepen, Stephan
%Y Sagae, Kenji
%Y Seddah, Djamé
%Y Sun, Weiwei
%Y Søgaard, Anders
%Y Tsarfaty, Reut
%Y Zeman, Dan
%S Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F hershcovich-etal-2020-kopsala
%X 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.
%R 10.18653/v1/2020.iwpt-1.25
%U https://aclanthology.org/2020.iwpt-1.25/
%U https://doi.org/10.18653/v1/2020.iwpt-1.25
%P 236-244
Markdown (Informal)
[Køpsala: Transition-Based Graph Parsing via Efficient Training and Effective Encoding](https://aclanthology.org/2020.iwpt-1.25/) (Hershcovich et al., IWPT 2020)
ACL