  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outdated, which may cause confusion in later conversations.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.
