Introducing MeMo: A Multimodal Dataset for Memory Modelling in Multiparty Conversations
<|reference_start|>Introducing MeMo: A Multimodal Dataset for Memory Modelling in Multiparty Conversations: The quality of human social relationships is intricately linked to human memory processes, with memory serving as the foundation for the creation of social bonds. Since human memory is selective, differing recollections of the same events within a group can lead to misunderstandings and misalignments in what is perceived to be common ground in the group. Yet, conversational facilitation systems, aimed at advancing the quality of group interactions, usually focus on tracking users' states within an individual session, ignoring what remains in each participant's memory after the interaction. Conversational memory is the process by which humans encode, retain and retrieve verbal, non-verbal and contextual information from a conversation. Understanding conversational memory can be used as a source of information on the long-term development of social connections within a group. This paper introduces the MeMo corpus, the first conversational dataset annotated with participants' memory retention reports, aimed at facilitating computational modelling of human conversational memory. The MeMo corpus includes 31 hours of small-group discussions on the topic of Covid-19, repeated over the term of 2 weeks. It integrates validated behavioural and perceptual measures, and includes audio, video, and multimodal annotations, offering a valuable resource for studying and modelling conversational memory and group dynamics. By introducing the MeMo corpus, presenting an analysis of its validity, and demonstrating its usefulness for future research, this paper aims to pave the way for future research in conversational memory modelling for intelligent system development.<|reference_end|>
@article{tsfasman2024introducing,
    title={Introducing MeMo: A Multimodal Dataset for Memory Modelling in
  Multiparty Conversations},
    author={Maria Tsfasman, Bernd Dudzik, Kristian Fenech, Andras Lorincz,
  Catholijn M. Jonker, Catharine Oertel},
    journal={arXiv preprint arXiv:2409.13715},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13715},
    primaryClass={cs.CL cs.AI cs.HC cs.LG}
}