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        <title>Joint End-to-end Semantic Proto-role Labeling</title>
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            <namePart type="given">Anna</namePart>
            <namePart type="family">Rogers</namePart>
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            <namePart type="given">Jordan</namePart>
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    <abstract>Semantic proto-role labeling (SPRL) assigns properties to arguments based on a series of binary labels. While multiple studies have evaluated various approaches to SPRL, it has only been studied in-depth as a standalone task using gold predicate/argument pairs. How do SPRL systems perform as part of an information extraction pipeline? We model SPRL jointly with predicate-argument extraction using a deep transformer model. We find that proto-role labeling is surprisingly robust in this setting, with only a small decrease when using predicted arguments. We include a detailed analysis of each component of the joint system, and an error analysis to understand correlations in errors between system stages. Finally, we study the effects of annotation errors on SPRL.</abstract>
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%0 Conference Proceedings
%T Joint End-to-end Semantic Proto-role Labeling
%A Spaulding, Elizabeth
%A Kazantsev, Gary
%A Dredze, Mark
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F spaulding-etal-2023-joint
%X Semantic proto-role labeling (SPRL) assigns properties to arguments based on a series of binary labels. While multiple studies have evaluated various approaches to SPRL, it has only been studied in-depth as a standalone task using gold predicate/argument pairs. How do SPRL systems perform as part of an information extraction pipeline? We model SPRL jointly with predicate-argument extraction using a deep transformer model. We find that proto-role labeling is surprisingly robust in this setting, with only a small decrease when using predicted arguments. We include a detailed analysis of each component of the joint system, and an error analysis to understand correlations in errors between system stages. Finally, we study the effects of annotation errors on SPRL.
%R 10.18653/v1/2023.acl-short.63
%U https://aclanthology.org/2023.acl-short.63
%U https://doi.org/10.18653/v1/2023.acl-short.63
%P 723-736

Markdown (Informal)

[Joint End-to-end Semantic Proto-role Labeling](https://aclanthology.org/2023.acl-short.63) (Spaulding et al., ACL 2023)

• Joint End-to-end Semantic Proto-role Labeling (Spaulding et al., ACL 2023)

ACL

• Elizabeth Spaulding, Gary Kazantsev, and Mark Dredze. 2023. Joint End-to-end Semantic Proto-role Labeling. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 723–736, Toronto, Canada. Association for Computational Linguistics.