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LS-DST: Long and Sparse Dialogue State Tracking with Smart History Collector in Insurance Marketing

Authors:

Ye Bi,

Xin YuAuthors Info & Claims

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1960 - 1964

https://doi.org/10.1145/3404835.3463058

Published: 11 July 2021 Publication History

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Abstract

Different from traditional task-oriented and open-domain dialogue systems, insurance agents aim to engage customers for helping them satisfy specific demands and emotional companionship. As a result, customer-to-agent dialogues are usually very long, and many turns of them are pure chit-chat without any useful marketing clues. This brings challenges to dialogue state tracking task in insurance marketing. To deal with these long and sparse dialogues, we propose a new dialogue state tracking architecture containing three components: dialogue encoder, Smart History Collector (SHC) and dialogue state classifier. SHC, a deliberately designed memory network, effectively selects relevant dialogue history via slot-attention, and then updates dialogue history memory. With SHC, our model is able to keep track of the vital information and filter out pure chit-chat. Experimental results demonstrate that our proposed LS-DST significantly outperforms the state-of-the-art baselines on real insurance dialogue dataset.

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Cited By

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Chen XSong XWei YNie LChua TChen HDuh WHuang HKato MMothe JPoblete B(2023)Dual Semantic Knowledge Composed Multimodal Dialog SystemsProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591673(1518-1527)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591673
Zhou JTian JWang RWu YYan MHe LHuang X(2023)Multi-modal multi-hop interaction network for dialogue response generationExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120267227:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120267

Index Terms

LS-DST: Long and Sparse Dialogue State Tracking with Smart History Collector in Insurance Marketing
1. Applied computing
  1. Computer forensics
2. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction

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Published In

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2021

2998 pages

ISBN:9781450380379

DOI:10.1145/3404835

General Chairs:
Fernando Diaz
(Google)
,
Chirag Shah
University of Washington
,
Torsten Suel
New York University
,
Program Chairs:
Pablo Castells
Universidad Autónoma de Madrid, Amazon
,
Rosie Jones
Spotify
,
Tetsuya Sakai
Waseda University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 July 2021

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SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2021

Virtual Event, Canada

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

View all

Chen XSong XWei YNie LChua TChen HDuh WHuang HKato MMothe JPoblete B(2023)Dual Semantic Knowledge Composed Multimodal Dialog SystemsProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591673(1518-1527)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591673
Zhou JTian JWang RWu YYan MHe LHuang X(2023)Multi-modal multi-hop interaction network for dialogue response generationExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120267227:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120267

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Explicit State Tracking with Semi-Supervisionfor Neural Dialogue Generation

Toward Real-life Dialogue State Tracking Involving Negative Feedback Utterances