MERCI: A Multimodal Dataset for Personalised and Emotionally-Aware Dialogues

The integration of conversational agents into daily life has become increasingly common. However, sustaining deeply engaging and natural interactions remains challenging due to a lack of multimodal datasets capturing personal and emotional nuances. In this paper, we introduce MERCI (Multimodal dataset for Emotionally-aware peRsonalised Conversational In-teractions), a dataset derived from user-robot dialogues involving thirty participants who completed user profile questionnaires covering ten personal topics (e.g., hobbies, music). A conver-sational system called PERCY then engaged with each partici-pant in open-domain conversations, leveraging GPT-4, real-time facial-expression and sentiment analysis to generate contextu-ally appropriate, empathetic responses. MERCI contains 1860 utterances, equating to about 12.5 hours of aligned audio, three-view video, transcripts with timestamps, emotion labels, and sentiment scores. This dataset serves as a reproducible test-bed for tasks such as emotion-aware response generation, multimodal affect recognition, and personalised policy learning. Baseline performance results have been established using advanced models such as BERT, T5, BART, and GPT-3.5/4/4o-mini across gener-ation, regression, and classification. Evaluations through human and automated methods have demonstrated strong naturalness, relevance, and consistency in responses while indicating areas for enhanced personalisation and empathic depth. We expect that MERCI will enhance the development of emotionally intelligent, user-centric conversational AI applications, potentially ranging from social robotics to mental health support.