Mixed-Session Conversation with Egocentric Memory
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.
6707b4eafd8b5c5c5056428c
Jihyoung Jang
admin_assigned
2024-10-10T11:11:17.269Z
6527f3f27ad7a346021075a7
/avatars/8f9e154b9426be73d7f4320ce4c6f342.svg
Jihyoung Jang
user
jihyoung
6707b4eafd8b5c5c5056428d
Taeyoung Kim
6707b4eafd8b5c5c5056428e
Hyounghun Kim
admin_assigned
2024-10-10T11:11:28.260Z
64d7162b3ca2924d6e8593e4
/avatars/51b28e4652bbb9d35adbd62aece59cd0.svg
Hyounghun Kim
user
khh3323
Mixed-Session Conversation with Egocentric Memory
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.
A new dialogue system, EMMA, with Egocentric Memory Enhanced Mixed-Session Conversation, uses the MiSC dataset to manage long-term dialogues with multiple partners seamlessly.
dialogue systems multi-session dialogue Mixed-Session Conversation MiSC Egocentric Memory Enhanced Mixed-Session Conversation Agent EMMA memory management conversational flow memorability