Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-based Question Answering

• URL: http://arxiv.org/abs/2004.03561v2
• Date: Sat, 23 May 2020 04:35:45 GMT
• Title: Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-based Question Answering
• Authors: Changmao Li, Jinho D. Choi
• Abstract summary: We introduce a novel approach to transformers that learns hierarchical representations in multiparty dialogue. Our approach is evaluated on the FriendsQA dataset and shows improvements of 3.8% and 1.4% over the two state-of-the-art transformer models BERT and RoBERTa.
• Score: 20.294478273161303
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a novel approach to transformers that learns hierarchical representations in multiparty dialogue. First, three language modeling tasks are used to pre-train the transformers, token- and utterance-level language modeling and utterance order prediction, that learn both token and utterance embeddings for better understanding in dialogue contexts. Then, multi-task learning between the utterance prediction and the token span prediction is applied to fine-tune for span-based question answering (QA). Our approach is evaluated on the FriendsQA dataset and shows improvements of 3.8% and 1.4% over the two state-of-the-art transformer models, BERT and RoBERTa, respectively.

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