@inproceedings{shi-etal-2020-semantic,
title = "Semantic Role Labeling as Syntactic Dependency Parsing",
author = "Shi, Tianze and
Malioutov, Igor and
Irsoy, Ozan",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.610",
doi = "10.18653/v1/2020.emnlp-main.610",
pages = "7551--7571",
abstract = "We reduce the task of (span-based) PropBank-style semantic role labeling (SRL) to syntactic dependency parsing. Our approach is motivated by our empirical analysis that shows three common syntactic patterns account for over 98{\%} of the SRL annotations for both English and Chinese data. Based on this observation, we present a conversion scheme that packs SRL annotations into dependency tree representations through joint labels that permit highly accurate recovery back to the original format. This representation allows us to train statistical dependency parsers to tackle SRL and achieve competitive performance with the current state of the art. Our findings show the promise of syntactic dependency trees in encoding semantic role relations within their syntactic domain of locality, and point to potential further integration of syntactic methods into semantic role labeling in the future.",
}
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<abstract>We reduce the task of (span-based) PropBank-style semantic role labeling (SRL) to syntactic dependency parsing. Our approach is motivated by our empirical analysis that shows three common syntactic patterns account for over 98% of the SRL annotations for both English and Chinese data. Based on this observation, we present a conversion scheme that packs SRL annotations into dependency tree representations through joint labels that permit highly accurate recovery back to the original format. This representation allows us to train statistical dependency parsers to tackle SRL and achieve competitive performance with the current state of the art. Our findings show the promise of syntactic dependency trees in encoding semantic role relations within their syntactic domain of locality, and point to potential further integration of syntactic methods into semantic role labeling in the future.</abstract>
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%0 Conference Proceedings
%T Semantic Role Labeling as Syntactic Dependency Parsing
%A Shi, Tianze
%A Malioutov, Igor
%A Irsoy, Ozan
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F shi-etal-2020-semantic
%X We reduce the task of (span-based) PropBank-style semantic role labeling (SRL) to syntactic dependency parsing. Our approach is motivated by our empirical analysis that shows three common syntactic patterns account for over 98% of the SRL annotations for both English and Chinese data. Based on this observation, we present a conversion scheme that packs SRL annotations into dependency tree representations through joint labels that permit highly accurate recovery back to the original format. This representation allows us to train statistical dependency parsers to tackle SRL and achieve competitive performance with the current state of the art. Our findings show the promise of syntactic dependency trees in encoding semantic role relations within their syntactic domain of locality, and point to potential further integration of syntactic methods into semantic role labeling in the future.
%R 10.18653/v1/2020.emnlp-main.610
%U https://aclanthology.org/2020.emnlp-main.610
%U https://doi.org/10.18653/v1/2020.emnlp-main.610
%P 7551-7571
Markdown (Informal)
[Semantic Role Labeling as Syntactic Dependency Parsing](https://aclanthology.org/2020.emnlp-main.610) (Shi et al., EMNLP 2020)
ACL
- Tianze Shi, Igor Malioutov, and Ozan Irsoy. 2020. Semantic Role Labeling as Syntactic Dependency Parsing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7551–7571, Online. Association for Computational Linguistics.