@inproceedings{ouchi-etal-2018-span,
title = "A Span Selection Model for Semantic Role Labeling",
author = "Ouchi, Hiroki and
Shindo, Hiroyuki and
Matsumoto, Yuji",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1191",
doi = "10.18653/v1/D18-1191",
pages = "1630--1642",
abstract = "We present a simple and accurate span-based model for semantic role labeling (SRL). Our model directly takes into account all possible argument spans and scores them for each label. At decoding time, we greedily select higher scoring labeled spans. One advantage of our model is to allow us to design and use span-level features, that are difficult to use in token-based BIO tagging approaches. Experimental results demonstrate that our ensemble model achieves the state-of-the-art results, 87.4 F1 and 87.0 F1 on the CoNLL-2005 and 2012 datasets, respectively.",
}
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<abstract>We present a simple and accurate span-based model for semantic role labeling (SRL). Our model directly takes into account all possible argument spans and scores them for each label. At decoding time, we greedily select higher scoring labeled spans. One advantage of our model is to allow us to design and use span-level features, that are difficult to use in token-based BIO tagging approaches. Experimental results demonstrate that our ensemble model achieves the state-of-the-art results, 87.4 F1 and 87.0 F1 on the CoNLL-2005 and 2012 datasets, respectively.</abstract>
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%0 Conference Proceedings
%T A Span Selection Model for Semantic Role Labeling
%A Ouchi, Hiroki
%A Shindo, Hiroyuki
%A Matsumoto, Yuji
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F ouchi-etal-2018-span
%X We present a simple and accurate span-based model for semantic role labeling (SRL). Our model directly takes into account all possible argument spans and scores them for each label. At decoding time, we greedily select higher scoring labeled spans. One advantage of our model is to allow us to design and use span-level features, that are difficult to use in token-based BIO tagging approaches. Experimental results demonstrate that our ensemble model achieves the state-of-the-art results, 87.4 F1 and 87.0 F1 on the CoNLL-2005 and 2012 datasets, respectively.
%R 10.18653/v1/D18-1191
%U https://aclanthology.org/D18-1191
%U https://doi.org/10.18653/v1/D18-1191
%P 1630-1642
Markdown (Informal)
[A Span Selection Model for Semantic Role Labeling](https://aclanthology.org/D18-1191) (Ouchi et al., EMNLP 2018)
ACL
- Hiroki Ouchi, Hiroyuki Shindo, and Yuji Matsumoto. 2018. A Span Selection Model for Semantic Role Labeling. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1630–1642, Brussels, Belgium. Association for Computational Linguistics.