Shihang Wang
2022
Long Time No See! Open-Domain Conversation with Long-Term Persona Memory
Xinchao Xu
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Zhibin Gou
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Wenquan Wu
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Zheng-Yu Niu
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Hua Wu
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Haifeng Wang
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Shihang Wang
Findings of the Association for Computational Linguistics: ACL 2022
Most of the open-domain dialogue models tend to perform poorly in the setting of long-term human-bot conversations. The possible reason is that they lack the capability of understanding and memorizing long-term dialogue history information. To address this issue, we present a novel task of Long-term Memory Conversation (LeMon) and then build a new dialogue dataset DuLeMon and a dialogue generation framework with Long-Term Memory (LTM) mechanism (called PLATO-LTM). This LTM mechanism enables our system to accurately extract and continuously update long-term persona memory without requiring multiple-session dialogue datasets for model training. To our knowledge, this is the first attempt to conduct real-time dynamic management of persona information of both parties, including the user and the bot. Results on DuLeMon indicate that PLATO-LTM can significantly outperform baselines in terms of long-term dialogue consistency, leading to better dialogue engagingness.
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Co-authors
- Xinchao Xu 1
- Zhibin Gou 1
- Wenquan Wu 1
- Zheng-Yu Niu 1
- Hua Wu 1
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