@inproceedings{yang-etal-2026-d2pcm,
title = "{D}2{PCM}:A Multi-Turn Dialogue Dataset with Personalized Contextual Memory",
author = "Yang, Zhe and
Huang, Yi and
Chen, Yaqin and
Gao, Chunyang and
Yao, Jingyu and
Feng, Junlan",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1870/",
pages = "37512--37529",
ISBN = "979-8-89176-395-1",
abstract = "Memory serves as a pivotal component in interactive response generation, supplying essential background information and referential knowledge for dialogues. Conventional interactive algorithms have predominantly treated memory as a merely contextual element, largely neglecting the nuanced cognitive processes involved in individualized memory encoding and retrieval. This conceptual gap has led to the prevailing schema where memory-enhanced dialogue datasets incorporate monolithic, undifferentiated memory content, failing to capture the personalized nature of persoa memory processing. Grounded in the self-reference effect from cognitive psychology, we introduce a Multi-Turn Dialogue Dataset with Personalized Contextual Memory (), establishing a comprehensive benchmark to facilitate advanced research on personalized memory processing algorithms."
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<abstract>Memory serves as a pivotal component in interactive response generation, supplying essential background information and referential knowledge for dialogues. Conventional interactive algorithms have predominantly treated memory as a merely contextual element, largely neglecting the nuanced cognitive processes involved in individualized memory encoding and retrieval. This conceptual gap has led to the prevailing schema where memory-enhanced dialogue datasets incorporate monolithic, undifferentiated memory content, failing to capture the personalized nature of persoa memory processing. Grounded in the self-reference effect from cognitive psychology, we introduce a Multi-Turn Dialogue Dataset with Personalized Contextual Memory (), establishing a comprehensive benchmark to facilitate advanced research on personalized memory processing algorithms.</abstract>
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%0 Conference Proceedings
%T D2PCM:A Multi-Turn Dialogue Dataset with Personalized Contextual Memory
%A Yang, Zhe
%A Huang, Yi
%A Chen, Yaqin
%A Gao, Chunyang
%A Yao, Jingyu
%A Feng, Junlan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F yang-etal-2026-d2pcm
%X Memory serves as a pivotal component in interactive response generation, supplying essential background information and referential knowledge for dialogues. Conventional interactive algorithms have predominantly treated memory as a merely contextual element, largely neglecting the nuanced cognitive processes involved in individualized memory encoding and retrieval. This conceptual gap has led to the prevailing schema where memory-enhanced dialogue datasets incorporate monolithic, undifferentiated memory content, failing to capture the personalized nature of persoa memory processing. Grounded in the self-reference effect from cognitive psychology, we introduce a Multi-Turn Dialogue Dataset with Personalized Contextual Memory (), establishing a comprehensive benchmark to facilitate advanced research on personalized memory processing algorithms.
%U https://aclanthology.org/2026.findings-acl.1870/
%P 37512-37529
Markdown (Informal)
[D2PCM:A Multi-Turn Dialogue Dataset with Personalized Contextual Memory](https://aclanthology.org/2026.findings-acl.1870/) (Yang et al., Findings 2026)
ACL