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    <titleInfo>
        <title>Document-Level Relation Extraction via Pair-Aware and Entity-Enhanced Representation Learning</title>
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    <name type="personal">
        <namePart type="given">Xiusheng</namePart>
        <namePart type="family">Huang</namePart>
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        <namePart type="given">Hang</namePart>
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    <name type="personal">
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        <namePart type="family">Chen</namePart>
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    <name type="personal">
        <namePart type="given">Jun</namePart>
        <namePart type="family">Zhao</namePart>
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    <name type="personal">
        <namePart type="given">Kang</namePart>
        <namePart type="family">Liu</namePart>
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    <name type="personal">
        <namePart type="given">Weijian</namePart>
        <namePart type="family">Sun</namePart>
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    <name type="personal">
        <namePart type="given">Zuyu</namePart>
        <namePart type="family">Zhao</namePart>
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            <title>Proceedings of the 29th International Conference on Computational Linguistics</title>
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    <abstract>Document-level relation extraction aims to recognize relations among multiple entity pairs from a whole piece of article. Recent methods achieve considerable performance but still suffer from two challenges: a) the relational entity pairs are sparse, b) the representation of entity pairs is insufficient. In this paper, we propose Pair-Aware and Entity-Enhanced(PAEE) model to solve the aforementioned two challenges. For the first challenge, we design a Pair-Aware Representation module to predict potential relational entity pairs, which constrains the relation extraction to the predicted entity pairs subset rather than all pairs; For the second, we introduce a Entity-Enhanced Representation module to assemble directional entity pairs and obtain a holistic understanding of the entire document. Experimental results show that our approach can obtain state-of-the-art performance on four benchmark datasets DocRED, DWIE, CDR and GDA.</abstract>
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        <url>https://aclanthology.org/2022.coling-1.213</url>
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        <date>2022-10</date>
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            <start>2418</start>
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%0 Conference Proceedings
%T Document-Level Relation Extraction via Pair-Aware and Entity-Enhanced Representation Learning
%A Huang, Xiusheng
%A Yang, Hang
%A Chen, Yubo
%A Zhao, Jun
%A Liu, Kang
%A Sun, Weijian
%A Zhao, Zuyu
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F huang-etal-2022-document
%X Document-level relation extraction aims to recognize relations among multiple entity pairs from a whole piece of article. Recent methods achieve considerable performance but still suffer from two challenges: a) the relational entity pairs are sparse, b) the representation of entity pairs is insufficient. In this paper, we propose Pair-Aware and Entity-Enhanced(PAEE) model to solve the aforementioned two challenges. For the first challenge, we design a Pair-Aware Representation module to predict potential relational entity pairs, which constrains the relation extraction to the predicted entity pairs subset rather than all pairs; For the second, we introduce a Entity-Enhanced Representation module to assemble directional entity pairs and obtain a holistic understanding of the entire document. Experimental results show that our approach can obtain state-of-the-art performance on four benchmark datasets DocRED, DWIE, CDR and GDA.
%U https://aclanthology.org/2022.coling-1.213
%P 2418-2428

Markdown (Informal)

[Document-Level Relation Extraction via Pair-Aware and Entity-Enhanced Representation Learning](https://aclanthology.org/2022.coling-1.213) (Huang et al., COLING 2022)

• Document-Level Relation Extraction via Pair-Aware and Entity-Enhanced Representation Learning (Huang et al., COLING 2022)

ACL

• Xiusheng Huang, Hang Yang, Yubo Chen, Jun Zhao, Kang Liu, Weijian Sun, and Zuyu Zhao. 2022. Document-Level Relation Extraction via Pair-Aware and Entity-Enhanced Representation Learning. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2418–2428, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.