@inproceedings{anderson-gomez-rodriguez-2021-modest,
title = "A Modest {P}areto Optimisation Analysis of Dependency Parsers in 2021",
author = "Anderson, Mark and
G{\'o}mez-Rodr{\'\i}guez, Carlos",
editor = "Oepen, Stephan and
Sagae, Kenji and
Tsarfaty, Reut and
Bouma, Gosse and
Seddah, Djam{\'e} and
Zeman, Daniel",
booktitle = "Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.iwpt-1.12",
doi = "10.18653/v1/2021.iwpt-1.12",
pages = "119--130",
abstract = "We evaluate three leading dependency parser systems from different paradigms on a small yet diverse subset of languages in terms of their accuracy-efficiency Pareto front. As we are interested in efficiency, we evaluate core parsers without pretrained language models (as these are typically huge networks and would constitute most of the compute time) or other augmentations that can be transversally applied to any of them. Biaffine parsing emerges as a well-balanced default choice, with sequence-labelling parsing being preferable if inference speed (but not training energy cost) is the priority.",
}
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<abstract>We evaluate three leading dependency parser systems from different paradigms on a small yet diverse subset of languages in terms of their accuracy-efficiency Pareto front. As we are interested in efficiency, we evaluate core parsers without pretrained language models (as these are typically huge networks and would constitute most of the compute time) or other augmentations that can be transversally applied to any of them. Biaffine parsing emerges as a well-balanced default choice, with sequence-labelling parsing being preferable if inference speed (but not training energy cost) is the priority.</abstract>
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%0 Conference Proceedings
%T A Modest Pareto Optimisation Analysis of Dependency Parsers in 2021
%A Anderson, Mark
%A Gómez-Rodríguez, Carlos
%Y Oepen, Stephan
%Y Sagae, Kenji
%Y Tsarfaty, Reut
%Y Bouma, Gosse
%Y Seddah, Djamé
%Y Zeman, Daniel
%S Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F anderson-gomez-rodriguez-2021-modest
%X We evaluate three leading dependency parser systems from different paradigms on a small yet diverse subset of languages in terms of their accuracy-efficiency Pareto front. As we are interested in efficiency, we evaluate core parsers without pretrained language models (as these are typically huge networks and would constitute most of the compute time) or other augmentations that can be transversally applied to any of them. Biaffine parsing emerges as a well-balanced default choice, with sequence-labelling parsing being preferable if inference speed (but not training energy cost) is the priority.
%R 10.18653/v1/2021.iwpt-1.12
%U https://aclanthology.org/2021.iwpt-1.12
%U https://doi.org/10.18653/v1/2021.iwpt-1.12
%P 119-130
Markdown (Informal)
[A Modest Pareto Optimisation Analysis of Dependency Parsers in 2021](https://aclanthology.org/2021.iwpt-1.12) (Anderson & Gómez-Rodríguez, IWPT 2021)
ACL
- Mark Anderson and Carlos Gómez-Rodríguez. 2021. A Modest Pareto Optimisation Analysis of Dependency Parsers in 2021. In Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021), pages 119–130, Online. Association for Computational Linguistics.