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    <titleInfo>
        <title>Graph Based Network with Contextualized Representations of Turns in Dialogue</title>
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    <name type="personal">
        <namePart type="given">Bongseok</namePart>
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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    <abstract>Dialogue-based relation extraction (RE) aims to extract relation(s) between two arguments that appear in a dialogue. Because dialogues have the characteristics of high personal pronoun occurrences and low information density, and since most relational facts in dialogues are not supported by any single sentence, dialogue-based relation extraction requires a comprehensive understanding of dialogue. In this paper, we propose the TUrn COntext awaRE Graph Convolutional Network (TUCORE-GCN) modeled by paying attention to the way people understand dialogues. In addition, we propose a novel approach which treats the task of emotion recognition in conversations (ERC) as a dialogue-based RE. Experiments on a dialogue-based RE dataset and three ERC datasets demonstrate that our model is very effective in various dialogue-based natural language understanding tasks. In these experiments, TUCORE-GCN outperforms the state-of-the-art models on most of the benchmark datasets. Our code is available at https://github.com/BlackNoodle/TUCORE-GCN.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.36</identifier>
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        <date>2021-11</date>
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%0 Conference Proceedings
%T Graph Based Network with Contextualized Representations of Turns in Dialogue
%A Lee, Bongseok
%A Choi, Yong Suk
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F lee-choi-2021-graph
%X Dialogue-based relation extraction (RE) aims to extract relation(s) between two arguments that appear in a dialogue. Because dialogues have the characteristics of high personal pronoun occurrences and low information density, and since most relational facts in dialogues are not supported by any single sentence, dialogue-based relation extraction requires a comprehensive understanding of dialogue. In this paper, we propose the TUrn COntext awaRE Graph Convolutional Network (TUCORE-GCN) modeled by paying attention to the way people understand dialogues. In addition, we propose a novel approach which treats the task of emotion recognition in conversations (ERC) as a dialogue-based RE. Experiments on a dialogue-based RE dataset and three ERC datasets demonstrate that our model is very effective in various dialogue-based natural language understanding tasks. In these experiments, TUCORE-GCN outperforms the state-of-the-art models on most of the benchmark datasets. Our code is available at https://github.com/BlackNoodle/TUCORE-GCN.
%R 10.18653/v1/2021.emnlp-main.36
%U https://aclanthology.org/2021.emnlp-main.36
%U https://doi.org/10.18653/v1/2021.emnlp-main.36
%P 443-455

Markdown (Informal)

[Graph Based Network with Contextualized Representations of Turns in Dialogue](https://aclanthology.org/2021.emnlp-main.36) (Lee & Choi, EMNLP 2021)

• Graph Based Network with Contextualized Representations of Turns in Dialogue (Lee & Choi, EMNLP 2021)

ACL

• Bongseok Lee and Yong Suk Choi. 2021. Graph Based Network with Contextualized Representations of Turns in Dialogue. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 443–455, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.