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    <name type="personal">
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            <namePart type="given">Kalika</namePart>
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    <abstract>Dialogue discourse parsing is a fundamental natural language processing task. It can benefit a series of conversation-related downstream tasks including dialogue summarization and emotion recognition in conversations. However, existing parsing approaches are constrained by predefined relation types, which can impede the adaptability of the parser for downstream tasks. To this end, we propose to introduce a task-aware paradigm to improve the versatility of the parser in this paper. Moreover, to alleviate error propagation and learning bias, we design a graph-based discourse parsing model termed DialogDP. Building upon the symmetrical property of matrix-embedded parsing graphs, we have developed an innovative self-supervised mechanism that leverages both bottom-up and top-down parsing strategies. This approach allows the parsing graphs to mutually regularize and enhance each other. Empirical studies on dialogue discourse parsing datasets and a downstream task demonstrate the effectiveness and flexibility of our framework.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.943</identifier>
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%0 Conference Proceedings
%T Task-Aware Self-Supervised Framework for Dialogue Discourse Parsing
%A Li, Wei
%A Zhu, Luyao
%A Shao, Wei
%A Yang, Zonglin
%A Cambria, Erik
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F li-etal-2023-task
%X Dialogue discourse parsing is a fundamental natural language processing task. It can benefit a series of conversation-related downstream tasks including dialogue summarization and emotion recognition in conversations. However, existing parsing approaches are constrained by predefined relation types, which can impede the adaptability of the parser for downstream tasks. To this end, we propose to introduce a task-aware paradigm to improve the versatility of the parser in this paper. Moreover, to alleviate error propagation and learning bias, we design a graph-based discourse parsing model termed DialogDP. Building upon the symmetrical property of matrix-embedded parsing graphs, we have developed an innovative self-supervised mechanism that leverages both bottom-up and top-down parsing strategies. This approach allows the parsing graphs to mutually regularize and enhance each other. Empirical studies on dialogue discourse parsing datasets and a downstream task demonstrate the effectiveness and flexibility of our framework.
%R 10.18653/v1/2023.findings-emnlp.943
%U https://aclanthology.org/2023.findings-emnlp.943
%U https://doi.org/10.18653/v1/2023.findings-emnlp.943
%P 14162-14173

Markdown (Informal)

[Task-Aware Self-Supervised Framework for Dialogue Discourse Parsing](https://aclanthology.org/2023.findings-emnlp.943) (Li et al., Findings 2023)

• Task-Aware Self-Supervised Framework for Dialogue Discourse Parsing (Li et al., Findings 2023)

ACL

• Wei Li, Luyao Zhu, Wei Shao, Zonglin Yang, and Erik Cambria. 2023. Task-Aware Self-Supervised Framework for Dialogue Discourse Parsing. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 14162–14173, Singapore. Association for Computational Linguistics.