Multiple Knowledge-Enhanced Interactive Graph Network for Multimodal Conversational Emotion Recognition (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Multimodal Emotion Recognition in Conversations models struggle due to lack of Common Sense Knowledge (CSK). |
| Approach: | They propose a multimodal approach to integrate multiple knowledge into the edge representations by integrating textual and visual CSK. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two popular datasets. |
Similar Papers
An Empirical Study on Multiple Knowledge from ChatGPT for Emotion Recognition in Conversations (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing efforts in ERC focus on context- and speaker-sensitive dependencies, but lack of annotated data and high cost of obtaining such knowledge is a blank slate. |
| Approach: | They propose a Multiple Knowledge Fusion Model to integrate multiple knowledge generated by Large Language Models (LLMs) they analyze the contribution and complementarity of this knowledge into the model. |
| Outcome: | The proposed model integrates multiple knowledge generated by LLMs and analyzes its contribution and complementarity on three public datasets. |
MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation (2021.acl-long)
Copied to clipboard
| Challenge: | Emotion recognition in conversation is a crucial component in affective dialogue systems, which helps the system understand users’ emotions and generate empathetic responses. |
| Approach: | They propose a multimodal fused graph convolutional network model which leverages multimodal dependencies and speaker information to model inter-speaker and intra-speech dependency. |
| Outcome: | The proposed model outperforms other SOTA methods on two public benchmark datasets, IEMOCAP and MELD. |
MMDAG: Multimodal Directed Acyclic Graph Network for Emotion Recognition in Conversation (2022.lrec-1)
Copied to clipboard
| Challenge: | Emotion recognition in conversation is important for an empathetic dialogue system to understand the user’s emotion and then generate appropriate emotional responses. |
| Approach: | They propose to use multimodal directed acyclic graphs to integrate multimodal information and contextual information into a DAG architecture to exploit multimodal contexts. |
| Outcome: | Comparative studies on IEMOCAP and MELD show that the proposed model outperforms state-of-the-art models. |
Affective Knowledge Enhanced Multiple-Graph Fusion Networks for Aspect-based Sentiment Analysis (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for sentiment analysis ignore the roles of syntax dependency relation labels and affective semantic information in determining the sentiment polarity of social media users. |
| Approach: | They propose a new multi-graph fusion network to leverage the richer syntax dependency relation labels and affective semantic information of words. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three datasets. |
Multimodal Emotion Recognition in Conversations: A Survey of Methods, Trends, Challenges and Prospects (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Multimodal Emotion Recognition in Conversations (MERC) is a new way to enhance human-computer interaction. |
| Approach: | This survey offers a systematic overview of Multimodal Emotion Recognition in Conversations . it examines motivations, core tasks, representative methods, and evaluation strategies . |
| Outcome: | The survey examines the effectiveness of MERC and its evaluation strategies. |
Dynamic Graph Neural ODE Network for Multi-modal Emotion Recognition in Conversation (2025.coling-main)
Copied to clipboard
| Challenge: | Existing graph-based multimodal emotion recognition methods fail to capture dynamic changes in emotions. |
| Approach: | They propose a Dynamic Graph Neural Ordinary Differential Equation Network (DGODE) which combines dynamic changes of emotions to capture temporal dependencies of speakers’ emotions. |
| Outcome: | The proposed model can capture the temporal dependencies caused by dynamic changes in emotions and can improve on two publicly available multimodal emotion recognition datasets. |
Multi-Condition Guided Diffusion Network for Multimodal Emotion Recognition in Conversation (2025.findings-naacl)
Copied to clipboard
| Challenge: | Current research emphasizes contextual factors, the speaker’s influence, and extracting complementary information across different modalities. |
| Approach: | They propose a diffusion-based approach to address the challenges posed by redundant information and redundant information at the semantic level while robustly capturing shared semantics. |
| Outcome: | The proposed model outperforms existing state-of-the-art models on two multimodal datasets and is generalizable and effective. |
Enhancing Emotion Recognition in Conversation via Multi-view Feature Alignment and Memorization (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Emotion recognition in conversation (ERC) is an advanced capability of conversational AI systems. |
| Approach: | They propose a semi-parametric paradigm for Emotion Recognition in conversation that uses supervised contrastive learning to align semantic-view and context-view features. |
| Outcome: | The proposed model achieves state-of-the-art on four widely used benchmarks. |
ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection (D18-1)
Copied to clipboard
| Challenge: | Existing studies do not explicitly consider inter-personal influences that thrive in the emotional dynamics of dialogues. |
| Approach: | They propose a multimodal emotion detection framework that extracts multimodal features from conversational videos and hierarchically models the self- and inter-speaker emotional influences into global memories. |
| Outcome: | The proposed model outperforms state-of-the-art networks on multiple classification and regression tasks in two benchmark datasets. |
Curriculum Learning Meets Directed Acyclic Graph for Multimodal Emotion Recognition (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing models for multimodal Emotion Recognition in conversation (ERC) use text as the main modality for emotion recognition. |
| Approach: | They propose a Directed Acyclic Graph (DAG) approach that integrates textual, acoustic, and visual features within a unified framework. |
| Outcome: | The proposed model outperforms baseline models on the IEMOCAP and MELD datasets. |