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        <title>Pre-training Multi-party Dialogue Models with Latent Discourse Inference</title>
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    <abstract>Multi-party dialogues are more difficult for models to understand than one-to-one two-party dialogues, since they involve multiple interlocutors, resulting in interweaving reply-to relations and information flows. To step over these obstacles, an effective way is to pre-train a model that understands the discourse structure of multi-party dialogues, namely, to whom each utterance is replying. However, due to the lack of explicitly annotated discourse labels in multi-party dialogue corpora, previous works fail to scale up the pre-training process by putting aside the unlabeled multi-party conversational data for nothing. To fully utilize the unlabeled data, we propose to treat the discourse structures as latent variables, then jointly infer them and pre-train the discourse-aware model by unsupervised latent variable inference methods. Experiments on multiple downstream tasks show that our pre-trained model outperforms strong baselines by large margins and achieves state-of-the-art (SOTA) results, justifying the effectiveness of our method. The official implementation of this paper is available at https://github.com/EricLee8/MPD_EMVI.</abstract>
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    <identifier type="doi">10.18653/v1/2023.acl-long.533</identifier>
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%0 Conference Proceedings
%T Pre-training Multi-party Dialogue Models with Latent Discourse Inference
%A Li, Yiyang
%A Huang, Xinting
%A Bi, Wei
%A Zhao, Hai
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F li-etal-2023-pre
%X Multi-party dialogues are more difficult for models to understand than one-to-one two-party dialogues, since they involve multiple interlocutors, resulting in interweaving reply-to relations and information flows. To step over these obstacles, an effective way is to pre-train a model that understands the discourse structure of multi-party dialogues, namely, to whom each utterance is replying. However, due to the lack of explicitly annotated discourse labels in multi-party dialogue corpora, previous works fail to scale up the pre-training process by putting aside the unlabeled multi-party conversational data for nothing. To fully utilize the unlabeled data, we propose to treat the discourse structures as latent variables, then jointly infer them and pre-train the discourse-aware model by unsupervised latent variable inference methods. Experiments on multiple downstream tasks show that our pre-trained model outperforms strong baselines by large margins and achieves state-of-the-art (SOTA) results, justifying the effectiveness of our method. The official implementation of this paper is available at https://github.com/EricLee8/MPD_EMVI.
%R 10.18653/v1/2023.acl-long.533
%U https://aclanthology.org/2023.acl-long.533
%U https://doi.org/10.18653/v1/2023.acl-long.533
%P 9584-9599

Markdown (Informal)

[Pre-training Multi-party Dialogue Models with Latent Discourse Inference](https://aclanthology.org/2023.acl-long.533) (Li et al., ACL 2023)

• Pre-training Multi-party Dialogue Models with Latent Discourse Inference (Li et al., ACL 2023)

ACL

• Yiyang Li, Xinting Huang, Wei Bi, and Hai Zhao. 2023. Pre-training Multi-party Dialogue Models with Latent Discourse Inference. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9584–9599, Toronto, Canada. Association for Computational Linguistics.