@inproceedings{bae-etal-2022-keep,
title = "Keep Me Updated! Memory Management in Long-term Conversations",
author = "Bae, Sanghwan and
Kwak, Donghyun and
Kang, Soyoung and
Lee, Min Young and
Kim, Sungdong and
Jeong, Yuin and
Kim, Hyeri and
Lee, Sang-Woo and
Park, Woomyoung and
Sung, Nako",
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.276",
doi = "10.18653/v1/2022.findings-emnlp.276",
pages = "3769--3787",
abstract = "Remembering important information from the past and continuing to talk about it in the present are crucial in long-term conversations. However, previous literature does not deal with cases where the memorized information is outdated, which may cause confusion in later conversations. To address this issue, we present a novel task and a corresponding dataset of memory management in long-term conversations, in which bots keep track of and bring up the latest information about users while conversing through multiple sessions. In order to support more precise and interpretable memory, we represent memory as unstructured text descriptions of key information and propose a new mechanism of memory management that selectively eliminates invalidated or redundant information. Experimental results show that our approach outperforms the baselines that leave the stored memory unchanged in terms of engagingness and humanness, with larger performance gap especially in the later sessions.",
}
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<abstract>Remembering important information from the past and continuing to talk about it in the present are crucial in long-term conversations. However, previous literature does not deal with cases where the memorized information is outdated, which may cause confusion in later conversations. To address this issue, we present a novel task and a corresponding dataset of memory management in long-term conversations, in which bots keep track of and bring up the latest information about users while conversing through multiple sessions. In order to support more precise and interpretable memory, we represent memory as unstructured text descriptions of key information and propose a new mechanism of memory management that selectively eliminates invalidated or redundant information. Experimental results show that our approach outperforms the baselines that leave the stored memory unchanged in terms of engagingness and humanness, with larger performance gap especially in the later sessions.</abstract>
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%0 Conference Proceedings
%T Keep Me Updated! Memory Management in Long-term Conversations
%A Bae, Sanghwan
%A Kwak, Donghyun
%A Kang, Soyoung
%A Lee, Min Young
%A Kim, Sungdong
%A Jeong, Yuin
%A Kim, Hyeri
%A Lee, Sang-Woo
%A Park, Woomyoung
%A Sung, Nako
%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 bae-etal-2022-keep
%X Remembering important information from the past and continuing to talk about it in the present are crucial in long-term conversations. However, previous literature does not deal with cases where the memorized information is outdated, which may cause confusion in later conversations. To address this issue, we present a novel task and a corresponding dataset of memory management in long-term conversations, in which bots keep track of and bring up the latest information about users while conversing through multiple sessions. In order to support more precise and interpretable memory, we represent memory as unstructured text descriptions of key information and propose a new mechanism of memory management that selectively eliminates invalidated or redundant information. Experimental results show that our approach outperforms the baselines that leave the stored memory unchanged in terms of engagingness and humanness, with larger performance gap especially in the later sessions.
%R 10.18653/v1/2022.findings-emnlp.276
%U https://aclanthology.org/2022.findings-emnlp.276
%U https://doi.org/10.18653/v1/2022.findings-emnlp.276
%P 3769-3787
Markdown (Informal)
[Keep Me Updated! Memory Management in Long-term Conversations](https://aclanthology.org/2022.findings-emnlp.276) (Bae et al., Findings 2022)
ACL
- Sanghwan Bae, Donghyun Kwak, Soyoung Kang, Min Young Lee, Sungdong Kim, Yuin Jeong, Hyeri Kim, Sang-Woo Lee, Woomyoung Park, and Nako Sung. 2022. Keep Me Updated! Memory Management in Long-term Conversations. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 3769–3787, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.