@inproceedings{gong-etal-2024-moddp,
title = "{MODDP}: A Multi-modal Open-domain {C}hinese Dataset for Dialogue Discourse Parsing",
author = "Gong, Chen and
Kong, DeXin and
Zhao, Suxian and
Li, Xingyu and
Fu, Guohong",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.628/",
doi = "10.18653/v1/2024.findings-acl.628",
pages = "10561--10573",
abstract = "Dialogue discourse parsing (DDP) aims to capture the relations between utterances in the dialogue. In everyday real-world scenarios, dialogues are typically multi-modal and cover open-domain topics. However, most existing widely used benchmark datasets for DDP contain only textual modality and are domain-specific. This makes it challenging to accurately and comprehensively understand the dialogue without multi-modal clues, and prevents them from capturing the discourse structures of the more prevalent daily conversations. This paper proposes MODDP, the first multi-modal Chinese discourse parsing dataset derived from open-domain daily dialogues, consisting 864 dialogues and 18,114 utterances, accompanied by 12.7 hours of video clips. We present a simple yet effective benchmark approach for multi-modal DDP. Through extensive experiments, we present several benchmark results based on MODDP. The significant improvement in performance from introducing multi-modalities into the original textual unimodal DDP model demonstrates the necessity of integrating multi-modalities into DDP."
}
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<abstract>Dialogue discourse parsing (DDP) aims to capture the relations between utterances in the dialogue. In everyday real-world scenarios, dialogues are typically multi-modal and cover open-domain topics. However, most existing widely used benchmark datasets for DDP contain only textual modality and are domain-specific. This makes it challenging to accurately and comprehensively understand the dialogue without multi-modal clues, and prevents them from capturing the discourse structures of the more prevalent daily conversations. This paper proposes MODDP, the first multi-modal Chinese discourse parsing dataset derived from open-domain daily dialogues, consisting 864 dialogues and 18,114 utterances, accompanied by 12.7 hours of video clips. We present a simple yet effective benchmark approach for multi-modal DDP. Through extensive experiments, we present several benchmark results based on MODDP. The significant improvement in performance from introducing multi-modalities into the original textual unimodal DDP model demonstrates the necessity of integrating multi-modalities into DDP.</abstract>
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%0 Conference Proceedings
%T MODDP: A Multi-modal Open-domain Chinese Dataset for Dialogue Discourse Parsing
%A Gong, Chen
%A Kong, DeXin
%A Zhao, Suxian
%A Li, Xingyu
%A Fu, Guohong
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics: ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F gong-etal-2024-moddp
%X Dialogue discourse parsing (DDP) aims to capture the relations between utterances in the dialogue. In everyday real-world scenarios, dialogues are typically multi-modal and cover open-domain topics. However, most existing widely used benchmark datasets for DDP contain only textual modality and are domain-specific. This makes it challenging to accurately and comprehensively understand the dialogue without multi-modal clues, and prevents them from capturing the discourse structures of the more prevalent daily conversations. This paper proposes MODDP, the first multi-modal Chinese discourse parsing dataset derived from open-domain daily dialogues, consisting 864 dialogues and 18,114 utterances, accompanied by 12.7 hours of video clips. We present a simple yet effective benchmark approach for multi-modal DDP. Through extensive experiments, we present several benchmark results based on MODDP. The significant improvement in performance from introducing multi-modalities into the original textual unimodal DDP model demonstrates the necessity of integrating multi-modalities into DDP.
%R 10.18653/v1/2024.findings-acl.628
%U https://aclanthology.org/2024.findings-acl.628/
%U https://doi.org/10.18653/v1/2024.findings-acl.628
%P 10561-10573
Markdown (Informal)
[MODDP: A Multi-modal Open-domain Chinese Dataset for Dialogue Discourse Parsing](https://aclanthology.org/2024.findings-acl.628/) (Gong et al., Findings 2024)
- MODDP: A Multi-modal Open-domain Chinese Dataset for Dialogue Discourse Parsing (Gong et al., Findings 2024)
ACL
- Chen Gong, DeXin Kong, Suxian Zhao, Xingyu Li, and Guohong Fu. 2024. MODDP: A Multi-modal Open-domain Chinese Dataset for Dialogue Discourse Parsing. In Findings of the Association for Computational Linguistics: ACL 2024, pages 10561–10573, Bangkok, Thailand. Association for Computational Linguistics.