Introducing MeMo: A Multimodal Dataset for Memory Modelling in Multiparty Conversations

TL;DR

This paper presents MeMo, a comprehensive multimodal dataset of group conversations with memory reports, enabling research on how humans encode, retain, and recall information in social interactions over time.

Contribution

The paper introduces the MeMo corpus, the first dataset with annotated memory retention reports in multiparty conversations, supporting computational modeling of conversational memory.

Findings

MeMo dataset includes 31 hours of discussions on Covid-19.

The dataset integrates behavioral, perceptual, audio, video, and multimodal annotations.

Analysis confirms the dataset's validity and potential for modeling conversational memory.

Abstract

Conversational memory is the process by which humans encode, retain and retrieve verbal, non-verbal and contextual information from a conversation. Since human memory is selective, differing recollections of the same events can lead to misunderstandings and misalignments within a 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. 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 Covid-19, repeated 3 times over the term of 2 weeks. It integrates validated behavioural and perceptual measures, audio, video, and multimodal annotations, offering a valuable resource for studying and modelling conversational memory and group dynamics. By introducing the MeMo corpus, analysing 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.