  Emotion recognition in conversation (ERC) is a crucial task in natural
language processing and affective computing. This paper proposes MultiDAG+CL, a
novel approach for Multimodal Emotion Recognition in Conversation (ERC) that
employs Directed Acyclic Graph (DAG) to integrate textual, acoustic, and visual
features within a unified framework. The model is enhanced by Curriculum
Learning (CL) to address challenges related to emotional shifts and data
imbalance. Curriculum learning facilitates the learning process by gradually
presenting training samples in a meaningful order, thereby improving the
model's performance in handling emotional variations and data imbalance.
Experimental results on the IEMOCAP and MELD datasets demonstrate that the
MultiDAG+CL models outperform baseline models. We release the code for
MultiDAG+CL and experiments: https://github.com/vanntc711/MultiDAG-CL
