Sequential Topic Selection Model with Latent Variable for Topic-Grounded Dialogue

Xiao-Fei Wen, Wei Wei, Xian-Ling Mao


Abstract
Recently, topic-grounded dialogue system has attracted significant attention due to its effectiveness in predicting the next topic to yield better responses via the historical context and given topic sequence. However, almost all existing topic prediction solutions focus on only the current conversation and corresponding topic sequence to predict the next conversation topic, without exploiting other topic-guided conversations which may contain relevant topic-transitions to current conversation. To address the problem, in this paper we propose a novel approach, named Sequential Global Topic Attention (SGTA) to exploit topic transition over all conversations in a subtle way for better modeling post-to-response topic-transition and guiding the response generation to the current conversation. Specifically, we introduce a latent space modeled as a Multivariate Skew-Normal distribution with hybrid kernel functions to flexibly integrate the global-level information with sequence-level information, and predict the topic based on the distribution sampling results. We also leverage a topic-aware prior-posterior approach for secondary selection of predicted topics, which is utilized to optimize the response generation task. Extensive experiments demonstrate that our model outperforms competitive baselines on prediction and generation tasks.
Anthology ID:
2022.findings-emnlp.87
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1209–1219
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.87
DOI:
10.18653/v1/2022.findings-emnlp.87
Bibkey:
Cite (ACL):
Xiao-Fei Wen, Wei Wei, and Xian-Ling Mao. 2022. Sequential Topic Selection Model with Latent Variable for Topic-Grounded Dialogue. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 1209–1219, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Sequential Topic Selection Model with Latent Variable for Topic-Grounded Dialogue (Wen et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-emnlp.87.pdf
Software:
 2022.findings-emnlp.87.software.zip
Dataset:
 2022.findings-emnlp.87.dataset.zip