@inproceedings{gu-etal-2022-empirical,
title = "An Empirical Study on Finding Spans",
author = "Gu, Weiwei and
Zheng, Boyuan and
Chen, Yunmo and
Chen, Tongfei and
Van Durme, Benjamin",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.264",
doi = "10.18653/v1/2022.emnlp-main.264",
pages = "3976--3983",
abstract = "We present an empirical study on methods for span finding, the selection of consecutive tokens in text for some downstream tasks. We focus on approaches that can be employed in training end-to-end information extraction systems, and find there is no definitive solution without considering task properties, and provide our observations to help with future design choices: 1) a tagging approach often yields higher precision while span enumeration and boundary prediction provide higher recall; 2) span type information can benefit a boundary prediction approach; 3) additional contextualization does not help span finding in most cases.",
}
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<abstract>We present an empirical study on methods for span finding, the selection of consecutive tokens in text for some downstream tasks. We focus on approaches that can be employed in training end-to-end information extraction systems, and find there is no definitive solution without considering task properties, and provide our observations to help with future design choices: 1) a tagging approach often yields higher precision while span enumeration and boundary prediction provide higher recall; 2) span type information can benefit a boundary prediction approach; 3) additional contextualization does not help span finding in most cases.</abstract>
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%0 Conference Proceedings
%T An Empirical Study on Finding Spans
%A Gu, Weiwei
%A Zheng, Boyuan
%A Chen, Yunmo
%A Chen, Tongfei
%A Van Durme, Benjamin
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F gu-etal-2022-empirical
%X We present an empirical study on methods for span finding, the selection of consecutive tokens in text for some downstream tasks. We focus on approaches that can be employed in training end-to-end information extraction systems, and find there is no definitive solution without considering task properties, and provide our observations to help with future design choices: 1) a tagging approach often yields higher precision while span enumeration and boundary prediction provide higher recall; 2) span type information can benefit a boundary prediction approach; 3) additional contextualization does not help span finding in most cases.
%R 10.18653/v1/2022.emnlp-main.264
%U https://aclanthology.org/2022.emnlp-main.264
%U https://doi.org/10.18653/v1/2022.emnlp-main.264
%P 3976-3983
Markdown (Informal)
[An Empirical Study on Finding Spans](https://aclanthology.org/2022.emnlp-main.264) (Gu et al., EMNLP 2022)
ACL
- Weiwei Gu, Boyuan Zheng, Yunmo Chen, Tongfei Chen, and Benjamin Van Durme. 2022. An Empirical Study on Finding Spans. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 3976–3983, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.