title: Mixed-Session Conversation with Egocentric Memory
summary: Recently introduced dialogue systems have demonstrated high usability.
  However, they still fall short of reflecting real-world conversation scenarios.
  Current dialogue systems exhibit an inability to replicate the dynamic,
  continuous, long-term interactions involving multiple partners. This shortfall
  arises because there have been limited efforts to account for both aspects of
  real-world dialogues: deeply layered interactions over the long-term dialogue
  and widely expanded conversation networks involving multiple participants. As
  the effort to incorporate these aspects combined, we introduce Mixed-Session
  Conversation, a dialogue system designed to construct conversations with
  various partners in a multi-session dialogue setup. We propose a new dataset
  called MiSC to implement this system. The dialogue episodes of MiSC consist of
  6 consecutive sessions, with four speakers (one main speaker and three
  partners) appearing in each episode. Also, we propose a new dialogue model with
  a novel memory management mechanism, called Egocentric Memory Enhanced
  Mixed-Session Conversation Agent (EMMA). EMMA collects and retains memories
  from the main speaker's perspective during conversations with partners,
  enabling seamless continuity in subsequent interactions. Extensive human
  evaluations validate that the dialogues in MiSC demonstrate a seamless
  conversational flow, even when conversation partners change in each session.
  EMMA trained with MiSC is also evaluated to maintain high memorability without
  contradiction throughout the entire conversation.
paper_authors._id: 6707b4eafd8b5c5c5056428c
paper_authors.name: Jihyoung Jang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-10T11:11:17.269Z
paper_authors._id: 6527f3f27ad7a346021075a7
paper_authors.avatarUrl: /avatars/8f9e154b9426be73d7f4320ce4c6f342.svg
paper_authors.fullname: Jihyoung Jang
paper_authors.type: user
paper_authors.user: jihyoung
paper_authors._id: 6707b4eafd8b5c5c5056428d
paper_authors.name: Taeyoung Kim
paper_authors._id: 6707b4eafd8b5c5c5056428e
paper_authors.name: Hyounghun Kim
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-10T11:11:28.260Z
paper_authors._id: 64d7162b3ca2924d6e8593e4
paper_authors.avatarUrl: /avatars/51b28e4652bbb9d35adbd62aece59cd0.svg
paper_authors.fullname: Hyounghun Kim
paper_authors.type: user
paper_authors.user: khh3323
paper_title: Mixed-Session Conversation with Egocentric Memory
paper_summary: Recently introduced dialogue systems have demonstrated high usability.
  However, they still fall short of reflecting real-world conversation scenarios.
  Current dialogue systems exhibit an inability to replicate the dynamic,
  continuous, long-term interactions involving multiple partners. This shortfall
  arises because there have been limited efforts to account for both aspects of
  real-world dialogues: deeply layered interactions over the long-term dialogue
  and widely expanded conversation networks involving multiple participants. As
  the effort to incorporate these aspects combined, we introduce Mixed-Session
  Conversation, a dialogue system designed to construct conversations with
  various partners in a multi-session dialogue setup. We propose a new dataset
  called MiSC to implement this system. The dialogue episodes of MiSC consist of
  6 consecutive sessions, with four speakers (one main speaker and three
  partners) appearing in each episode. Also, we propose a new dialogue model with
  a novel memory management mechanism, called Egocentric Memory Enhanced
  Mixed-Session Conversation Agent (EMMA). EMMA collects and retains memories
  from the main speaker's perspective during conversations with partners,
  enabling seamless continuity in subsequent interactions. Extensive human
  evaluations validate that the dialogues in MiSC demonstrate a seamless
  conversational flow, even when conversation partners change in each session.
  EMMA trained with MiSC is also evaluated to maintain high memorability without
  contradiction throughout the entire conversation.
paper_ai_keywords: Mixed-Session Conversation MiSC (dataset) Egocentric Memory Enhanced Mixed-Session Conversation Agent (EMMA) memory management memorability