# GraphMFT: A Graph Network based Multimodal Fusion Technique for Emotion Recognition in Conversation

arXiv: https://arxiv.org/abs/2208.00339 · HTML: https://mlsanity.com/paper/2208.00339/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2022.

## Built on by (most cited first, 8 of 8)

- [A Low-rank Matching Attention based Cross-modal Feature Fusion Method for Conversational Emotion Recognition](https://mlsanity.com/paper/2306.17799/) (2023)
- [Conversation Understanding using Relational Temporal Graph Neural Networks with Auxiliary Cross-Modality Interaction](https://mlsanity.com/paper/2311.04507/) (2023)
- [LineConGraphs: Line Conversation Graphs for Effective Emotion Recognition using Graph Neural Networks](https://mlsanity.com/paper/2312.03756/) (2023)
- [Clustering-Oriented Generative Attribute Graph Imputation](https://mlsanity.com/paper/2507.19085/) (2025)
- [Decoding Student Minds: Leveraging Conversational Agents for Psychological and Learning Analysis](https://mlsanity.com/paper/2512.10441/) (2025)
- [CLARGA: Multimodal Graph Representation Learning over Arbitrary Sets of Modalities](https://mlsanity.com/paper/2512.11901/) (2025)
- [Low resource cross-modal alignment using HGNN to enhance speech representation](https://mlsanity.com/paper/2609.23191/) (2026)
- [Enhancing speech representation learning with cross-modal knowledge transfer with HGNN under low resource settings: the case study of Yemba](https://mlsanity.com/paper/2609.23194/) (2026)

## Abstract (arXiv)

Multimodal machine learning is an emerging area of research, which has received a great deal of scholarly attention in recent years. Up to now, there are few studies on multimodal Emotion Recognition in Conversation (ERC). Since Graph Neural Networks (GNNs) possess the powerful capacity of relational modeling, they have an inherent advantage in the field of multimodal learning. GNNs leverage the graph constructed from multimodal data to perform intra- and inter-modal information interaction, which effectively facilitates the integration and complementation of multimodal data. In this work, we propose a novel Graph network based Multimodal Fusion Technique (GraphMFT) for emotion recognition in conversation. Multimodal data can be modeled as a graph, where each data object is regarded as a node, and both intra- and inter-modal dependencies existing between data objects can be regarded as edges. GraphMFT utilizes multiple improved graph attention networks to capture intra-modal contextual information and inter-modal complementary information. In addition, the proposed GraphMFT attempts to address the challenges of existing graph-based multimodal conversational emotion recognition models such as MMGCN. Empirical results on two public multimodal datasets reveal that our model outperforms the State-Of-The-Art (SOTA) approaches with the accuracy of 67.90% and 61.30%.