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            <namePart type="given">Kam-Fai</namePart>
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    <abstract>Current end-to-end semantic role labeling is mostly accomplished via graph-based neural models. However, these all are first-order models, where each decision for detecting any predicate-argument pair is made in isolation with local features. In this paper, we present a high-order refining mechanism to perform interaction between all predicate-argument pairs. Based on the baseline graph model, our high-order refining module learns higher-order features between all candidate pairs via attention calculation, which are later used to update the original token representations. After several iterations of refinement, the underlying token representations can be enriched with globally interacted features. Our high-order model achieves state-of-the-art results on Chinese SRL data, including CoNLL09 and Universal Proposition Bank, meanwhile relieving the long-range dependency issues.</abstract>
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%0 Conference Proceedings
%T High-order Refining for End-to-end Chinese Semantic Role Labeling
%A Fei, Hao
%A Ren, Yafeng
%A Ji, Donghong
%Y Wong, Kam-Fai
%Y Knight, Kevin
%Y Wu, Hua
%S Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing
%D 2020
%8 December
%I Association for Computational Linguistics
%C Suzhou, China
%F fei-etal-2020-high
%X Current end-to-end semantic role labeling is mostly accomplished via graph-based neural models. However, these all are first-order models, where each decision for detecting any predicate-argument pair is made in isolation with local features. In this paper, we present a high-order refining mechanism to perform interaction between all predicate-argument pairs. Based on the baseline graph model, our high-order refining module learns higher-order features between all candidate pairs via attention calculation, which are later used to update the original token representations. After several iterations of refinement, the underlying token representations can be enriched with globally interacted features. Our high-order model achieves state-of-the-art results on Chinese SRL data, including CoNLL09 and Universal Proposition Bank, meanwhile relieving the long-range dependency issues.
%U https://aclanthology.org/2020.aacl-main.13
%P 100-105

Markdown (Informal)

[High-order Refining for End-to-end Chinese Semantic Role Labeling](https://aclanthology.org/2020.aacl-main.13) (Fei et al., AACL 2020)

• High-order Refining for End-to-end Chinese Semantic Role Labeling (Fei et al., AACL 2020)

ACL

• Hao Fei, Yafeng Ren, and Donghong Ji. 2020. High-order Refining for End-to-end Chinese Semantic Role Labeling. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pages 100–105, Suzhou, China. Association for Computational Linguistics.