Pre-training Multi-party Dialogue Models with Latent Discourse Inference

• URL: http://arxiv.org/abs/2305.15175v1
• Date: Wed, 24 May 2023 14:06:27 GMT
• Title: Pre-training Multi-party Dialogue Models with Latent Discourse Inference
• Authors: Yiyang Li, Xinting Huang, Wei Bi, Hai Zhao
• Abstract summary: We pre-train a model that understands the discourse structure of multi-party dialogues, namely, to whom each utterance is replying. 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.
• Score: 85.9683181507206
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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