A Hybrid Conversational Agent with Semantic Association of Autobiographic Memories for the Elderly

Hsiao, Yu‐Ting; Gamborino, Edwinn; Fu, Li‐Chen

doi:10.1007/978-3-030-49913-6_5

Cited by 2 publications

(1 citation statement)

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“…Representing latent features as neural memory (Weston et al, 2015;Tran et al, 2016;Munkhdalai et al, 2019) used to be a traditional method. Slot-value format in dialogue state tracking (Heck et al, 2020;Hosseini-Asl et al, 2020;Kim et al, 2020), and graph format in Hsiao et al (2020) have been the two major approaches in handling the memorized information in a structured way. Kim et al (2020) suggested update operations on fixed-sized slot-value pairs for dialogue states.…”

Section: Long-term Memory In Conversationmentioning

confidence: 99%

Keep Me Updated! Memory Management in Long-term Conversations

Bae¹,

Kwak²,

Kang³

et al. 2022

Preprint

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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 However, previous literature does not deal with cases where the memorized information is outdated, which may cause confusion in later conversations.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 longterm 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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