@inproceedings{zhang-etal-2022-history,
title = "History-Aware Hierarchical Transformer for Multi-session Open-domain Dialogue System",
author = "Zhang, Tong and
Liu, Yong and
Li, Boyang and
Zeng, Zhiwei and
Wang, Pengwei and
You, Yuan and
Miao, Chunyan and
Cui, Lizhen",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.247",
doi = "10.18653/v1/2022.findings-emnlp.247",
pages = "3395--3407",
abstract = "With the evolution of pre-trained language models, current open-domain dialogue systems have achieved great progress in conducting one-session conversations. In contrast, Multi-Session Conversation (MSC), which consists of multiple sessions over a long term with the same user, is under-investigated. In this paper, we propose History-Aware Hierarchical Transformer (HAHT) for multi-session open-domain dialogue. HAHT maintains a long-term memory of history conversations and utilizes history information to understand current conversation context and generate well-informed and context-relevant responses. Specifically, HAHT first encodes history conversation sessions hierarchically into a history memory. Then, HAHT leverages historical information to facilitate the understanding of the current conversation context by encoding the history memory together with the current context with attention-based mechanisms. Finally, to explicitly utilize historical information, HAHT uses a history-aware response generator that switches between a generic vocabulary and a history-aware vocabulary. Experimental results on a large-scale MSC dataset suggest that the proposed HAHT model consistently outperforms baseline models. Human evaluation results support that HAHT generates more human-like, context-relevant, and history-relevant responses than baseline models.",
}
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<abstract>With the evolution of pre-trained language models, current open-domain dialogue systems have achieved great progress in conducting one-session conversations. In contrast, Multi-Session Conversation (MSC), which consists of multiple sessions over a long term with the same user, is under-investigated. In this paper, we propose History-Aware Hierarchical Transformer (HAHT) for multi-session open-domain dialogue. HAHT maintains a long-term memory of history conversations and utilizes history information to understand current conversation context and generate well-informed and context-relevant responses. Specifically, HAHT first encodes history conversation sessions hierarchically into a history memory. Then, HAHT leverages historical information to facilitate the understanding of the current conversation context by encoding the history memory together with the current context with attention-based mechanisms. Finally, to explicitly utilize historical information, HAHT uses a history-aware response generator that switches between a generic vocabulary and a history-aware vocabulary. Experimental results on a large-scale MSC dataset suggest that the proposed HAHT model consistently outperforms baseline models. Human evaluation results support that HAHT generates more human-like, context-relevant, and history-relevant responses than baseline models.</abstract>
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%0 Conference Proceedings
%T History-Aware Hierarchical Transformer for Multi-session Open-domain Dialogue System
%A Zhang, Tong
%A Liu, Yong
%A Li, Boyang
%A Zeng, Zhiwei
%A Wang, Pengwei
%A You, Yuan
%A Miao, Chunyan
%A Cui, Lizhen
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F zhang-etal-2022-history
%X With the evolution of pre-trained language models, current open-domain dialogue systems have achieved great progress in conducting one-session conversations. In contrast, Multi-Session Conversation (MSC), which consists of multiple sessions over a long term with the same user, is under-investigated. In this paper, we propose History-Aware Hierarchical Transformer (HAHT) for multi-session open-domain dialogue. HAHT maintains a long-term memory of history conversations and utilizes history information to understand current conversation context and generate well-informed and context-relevant responses. Specifically, HAHT first encodes history conversation sessions hierarchically into a history memory. Then, HAHT leverages historical information to facilitate the understanding of the current conversation context by encoding the history memory together with the current context with attention-based mechanisms. Finally, to explicitly utilize historical information, HAHT uses a history-aware response generator that switches between a generic vocabulary and a history-aware vocabulary. Experimental results on a large-scale MSC dataset suggest that the proposed HAHT model consistently outperforms baseline models. Human evaluation results support that HAHT generates more human-like, context-relevant, and history-relevant responses than baseline models.
%R 10.18653/v1/2022.findings-emnlp.247
%U https://aclanthology.org/2022.findings-emnlp.247
%U https://doi.org/10.18653/v1/2022.findings-emnlp.247
%P 3395-3407
Markdown (Informal)
[History-Aware Hierarchical Transformer for Multi-session Open-domain Dialogue System](https://aclanthology.org/2022.findings-emnlp.247) (Zhang et al., Findings 2022)
ACL
- Tong Zhang, Yong Liu, Boyang Li, Zhiwei Zeng, Pengwei Wang, Yuan You, Chunyan Miao, and Lizhen Cui. 2022. History-Aware Hierarchical Transformer for Multi-session Open-domain Dialogue System. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 3395–3407, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.