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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 16: AAAI-21 Technical Tracks 16

Topic-Aware Multi-turn Dialogue Modeling

February 1, 2023

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Authors

Yi Xu

Department of Computer Science and Engineering, Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University, Shanghai, China MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University


Hai Zhao

Department of Computer Science and Engineering, Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University, Shanghai, China MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University


Zhuosheng Zhang

Department of Computer Science and Engineering, Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University, Shanghai, China MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University


DOI:

10.1609/aaai.v35i16.17668


Abstract:

In 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.

Topics: AAAI

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HOW TO CITE:

Yi Xu||Hai Zhao||Zhuosheng Zhang Topic-Aware Multi-turn Dialogue Modeling Proceedings of the AAAI Conference on Artificial Intelligence (2021) 14176-14184.

Yi Xu||Hai Zhao||Zhuosheng Zhang Topic-Aware Multi-turn Dialogue Modeling AAAI 2021, 14176-14184.

Yi Xu||Hai Zhao||Zhuosheng Zhang (2021). Topic-Aware Multi-turn Dialogue Modeling. Proceedings of the AAAI Conference on Artificial Intelligence, 14176-14184.

Yi Xu||Hai Zhao||Zhuosheng Zhang. Topic-Aware Multi-turn Dialogue Modeling. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.14176-14184.

Yi Xu||Hai Zhao||Zhuosheng Zhang. 2021. Topic-Aware Multi-turn Dialogue Modeling. "Proceedings of the AAAI Conference on Artificial Intelligence". 14176-14184.

Yi Xu||Hai Zhao||Zhuosheng Zhang. (2021) "Topic-Aware Multi-turn Dialogue Modeling", Proceedings of the AAAI Conference on Artificial Intelligence, p.14176-14184

Yi Xu||Hai Zhao||Zhuosheng Zhang, "Topic-Aware Multi-turn Dialogue Modeling", AAAI, p.14176-14184, 2021.

Yi Xu||Hai Zhao||Zhuosheng Zhang. "Topic-Aware Multi-turn Dialogue Modeling". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.14176-14184.

Yi Xu||Hai Zhao||Zhuosheng Zhang. "Topic-Aware Multi-turn Dialogue Modeling". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 14176-14184.

Yi Xu||Hai Zhao||Zhuosheng Zhang. Topic-Aware Multi-turn Dialogue Modeling. AAAI[Internet]. 2021[cited 2023]; 14176-14184.


ISSN: 2374-3468


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