Topic-Aware Multi-turn Dialogue Modeling
Keywords:Conversational AI/Dialog Systems
AbstractIn the retrieval-based multi-turn dialogue modeling, it remains a challenge to select the most appropriate response according to extracting salient features in context utterances. As a conversation goes on, topic shift at discourse-level naturally happens through the continuous multi-turn dialogue context. However, all known retrieval-based systems are satisfied with exploiting local topic words for context utterance representation but fail to capture such essential global topic-aware clues at discourse-level. Instead of taking topic-agnostic n-gram utterance as processing unit for matching purpose in existing systems, this paper presents a novel topic-aware solution for multi-turn dialogue modeling, which segments and extracts topic-aware utterances in an unsupervised way, so that the resulted model is capable of capturing salient topic shift at discourse-level in need and thus effectively track topic flow during multi-turn conversation. Our topic-aware modeling is implemented by a newly proposed unsupervised topic-aware segmentation algorithm and Topic-Aware Dual-attention Matching (TADAM) Network, which matches each topic segment with the response in a dual cross-attention way. Experimental results on three public datasets show TADAM can outperform the state-of-the-art method, especially by 3.3% on E-commerce dataset that has an obvious topic shift.
How to Cite
Xu, Y., Zhao, H., & Zhang, Z. (2021). Topic-Aware Multi-turn Dialogue Modeling. Proceedings of the AAAI Conference on Artificial Intelligence, 35(16), 14176-14184. https://doi.org/10.1609/aaai.v35i16.17668
AAAI Technical Track on Speech and Natural Language Processing III