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

arXiv: https://arxiv.org/abs/2409.13715 · HTML: https://mlsanity.com/paper/2409.13715/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 107 references that are in the map.
- First posted on arXiv 7 Sep 2024.

## Builds on (most cited first, 7 of 7)

- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [Data and its (dis)contents: A survey of dataset development and use in machine learning research](https://mlsanity.com/paper/2012.05345/) (2020)
- [What Makes a Good Conversation? Challenges in Designing Truly Conversational Agents](https://mlsanity.com/paper/1901.06525/) (2019)
- [Automatic Text-based Personality Recognition on Monologues and Multiparty Dialogues Using Attentive Networks and Contextual Embeddings](https://mlsanity.com/paper/1911.09304/) (2019)
- [VideoMem: Constructing, Analyzing, Predicting Short-term and Long-term Video Memorability](https://mlsanity.com/paper/1812.01973/) (2018)
- [Multimodal Memorability: Modeling Effects of Semantics and Decay on Video Memorability](https://mlsanity.com/paper/2009.02568/) (2020)
- [Experiences from the MediaEval Predicting Media Memorability Task](https://mlsanity.com/paper/2212.03955/) (2022)

## Built on by (most cited first, 1 of 1)

- [The Emotion-Memory Link: Do Memorability Annotations Matter for Intelligent Systems?](https://mlsanity.com/paper/2507.14084/) (2025)

## Abstract (arXiv)

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.