# Curriculum Learning Meets Directed Acyclic Graph for Multimodal Emotion Recognition

arXiv: https://arxiv.org/abs/2402.17269 · HTML: https://mlsanity.com/paper/2402.17269/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 27 Feb 2024.

## Builds on (most cited first, 9 of 9)

- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [DAG-GNN: DAG Structure Learning with Graph Neural Networks](https://mlsanity.com/paper/1904.10098/) (2019)
- [DialogueRNN: An Attentive RNN for Emotion Detection in Conversations](https://mlsanity.com/paper/1811.00405/) (2018)
- [DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation](https://mlsanity.com/paper/1908.11540/) (2019)
- [Memory Fusion Network for Multi-view Sequential Learning](https://mlsanity.com/paper/1802.00927/) (2018)
- [MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation](https://mlsanity.com/paper/2107.06779/) (2021)
- [Affect-LM: A Neural Language Model for Customizable Affective Text Generation](https://mlsanity.com/paper/1704.06851/) (2017)
- [Hybrid Curriculum Learning for Emotion Recognition in Conversation](https://mlsanity.com/paper/2112.11718/) (2021)
- [Conversation Understanding using Relational Temporal Graph Neural Networks with Auxiliary Cross-Modality Interaction](https://mlsanity.com/paper/2311.04507/) (2023)

## Built on by (most cited first, 5 of 5)

- [Long-Short Distance Graph Neural Networks and Improved Curriculum Learning for Emotion Recognition in Conversation](https://mlsanity.com/paper/2507.15205/) (2025)
- [EII-SCL: Harnessing Emotional Inertia for Multimodal Emotion Recognition in Conversation](https://mlsanity.com/paper/2607.17366/) (2026)
- [EmoEUS: Uncertainty Supervision for Multimodal Emotion Recognition in Conversation](https://mlsanity.com/paper/2607.18336/) (2026)
- [Divide and Refine: Enhancing Multimodal Representation and Explainability for Emotion Recognition in Conversation](https://mlsanity.com/paper/2601.14274/) (2026)
- [Leveraging Self-Paced Curriculum Learning for Enhanced Modality Balance in Multimodal Conversational Emotion Recognition](https://mlsanity.com/paper/2605.21565/) (2026)

## Abstract (arXiv)

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