Keep Me Updated! Memory Management in Long-term Conversations

TL;DR

This paper introduces a new task and dataset for memory management in long-term conversations, focusing on updating and maintaining relevant information across multiple sessions to improve chatbot engagement and humanness.

Contribution

It proposes a novel memory management mechanism that selectively removes outdated or redundant information, enhancing long-term conversational consistency.

Findings

Outperforms baselines in engagingness and humanness

Larger performance gap in later sessions

Supports more precise and interpretable memory

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 However, previous literature does not deal with cases where the memorized information is outdated, which may cause confusion in later conversations.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