Effective Context Modeling Framework for Emotion Recognition in Conversations

• URL: http://arxiv.org/abs/2412.16444v1
• Date: Sat, 21 Dec 2024 02:22:06 GMT
• Title: Effective Context Modeling Framework for Emotion Recognition in Conversations
• Authors: Cuong Tran Van, Thanh V. T. Tran, Van Nguyen, Truong Son Hy,
• Abstract summary: Emotion Recognition in Conversations (ERC) facilitates a deeper understanding of the emotions conveyed by speakers in each utterance within a conversation. Recent Graph Neural Networks (GNNs) have demonstrated their strengths in capturing data relationships. We propose ConxGNN, a novel GNN-based framework designed to capture contextual information in conversations.
• Score: 2.7175580940471913
• License:
• Abstract: Emotion Recognition in Conversations (ERC) facilitates a deeper understanding of the emotions conveyed by speakers in each utterance within a conversation. Recently, Graph Neural Networks (GNNs) have demonstrated their strengths in capturing data relationships, particularly in contextual information modeling and multimodal fusion. However, existing methods often struggle to fully capture the complex interactions between multiple modalities and conversational context, limiting their expressiveness. To overcome these limitations, we propose ConxGNN, a novel GNN-based framework designed to capture contextual information in conversations. ConxGNN features two key parallel modules: a multi-scale heterogeneous graph that captures the diverse effects of utterances on emotional changes, and a hypergraph that models the multivariate relationships among modalities and utterances. The outputs from these modules are integrated into a fusion layer, where a cross-modal attention mechanism is applied to produce a contextually enriched representation. Additionally, ConxGNN tackles the challenge of recognizing minority or semantically similar emotion classes by incorporating a re-weighting scheme into the loss functions. Experimental results on the IEMOCAP and MELD benchmark datasets demonstrate the effectiveness of our method, achieving state-of-the-art performance compared to previous baselines.

Related papers

• Hierarchical Banzhaf Interaction for General Video-Language Representation Learning [60.44337740854767]
  Multimodal representation learning plays an important role in the artificial intelligence domain. We introduce a new approach that models video-text as game players using multivariate cooperative game theory. We extend our original structure into a flexible encoder-decoder framework, enabling the model to adapt to various downstream tasks.
  arXiv  Detail & Related papers  (2024-12-30T14:09:15Z)
• SDR-GNN: Spectral Domain Reconstruction Graph Neural Network for Incomplete Multimodal Learning in Conversational Emotion Recognition [14.645598552036908]
  Multimodal Emotion Recognition in Conversations (MERC) aims to classify utterance emotions using textual, auditory, and visual modal features. Most existing MERC methods assume each utterance has complete modalities, overlooking the common issue of incomplete modalities in real-world scenarios. We propose a Spectral Domain Reconstruction Graph Neural Network (SDR-GNN) for incomplete multimodal learning in conversational emotion recognition.
  arXiv  Detail & Related papers  (2024-11-29T16:31:50Z)
• Efficient Long-distance Latent Relation-aware Graph Neural Network for Multi-modal Emotion Recognition in Conversations [8.107561045241445]
  We propose an Efficient Long-distance Latent Relation-aware Graph Neural Network (ELR-GNN) for multi-modal emotion recognition in conversations. ELR-GNN achieves state-of-the-art performance on the benchmark IEMOCAP and MELD, with running times reduced by 52% and 35%, respectively.
  arXiv  Detail & Related papers  (2024-06-27T15:54:12Z)
• AIMDiT: Modality Augmentation and Interaction via Multimodal Dimension Transformation for Emotion Recognition in Conversations [57.99479708224221]
  We propose a novel framework called AIMDiT to solve the problem of multimodal fusion of deep features. Experiments conducted using our AIMDiT framework on the public benchmark dataset MELD reveal 2.34% and 2.87% improvements in terms of the Acc-7 and w-F1 metrics.
  arXiv  Detail & Related papers  (2024-04-12T11:31:18Z)
• DER-GCN: Dialogue and Event Relation-Aware Graph Convolutional Neural Network for Multimodal Dialogue Emotion Recognition [14.639340916340801]
  We propose a novel Dialogue and Event Relation-Aware Graph Convolutional Neural Network for Multimodal Emotion Recognition (DER-GCN) method. It models dialogue relations between speakers and captures latent event relations information. We conduct extensive experiments on the IEMOCAP and MELD benchmark datasets, which verify the effectiveness of the DER-GCN model.
  arXiv  Detail & Related papers  (2023-12-17T01:49:40Z)
• Conversation Understanding using Relational Temporal Graph Neural Networks with Auxiliary Cross-Modality Interaction [2.1261712640167856]
  Emotion recognition is a crucial task for human conversation understanding. We propose an input Temporal Graph Neural Network with Cross-Modality Interaction (CORECT) CORECT effectively captures conversation-level cross-modality interactions and utterance-level temporal dependencies.
  arXiv  Detail & Related papers  (2023-11-08T07:46:25Z)
• Capturing Spectral and Long-term Contextual Information for Speech Emotion Recognition Using Deep Learning Techniques [0.0]
  This research proposes an ensemble model that combines Graph Convolutional Networks (GCN) for processing textual data and the HuBERT transformer for analyzing audio signals. By combining GCN and HuBERT, our ensemble model can leverage the strengths of both approaches. Results indicate that the combined model can overcome the limitations of traditional methods, leading to enhanced accuracy in recognizing emotions from speech.
  arXiv  Detail & Related papers  (2023-08-04T06:20:42Z)
• Re-mine, Learn and Reason: Exploring the Cross-modal Semantic Correlations for Language-guided HOI detection [57.13665112065285]
  Human-Object Interaction (HOI) detection is a challenging computer vision task. We present a framework that enhances HOI detection by incorporating structured text knowledge.
  arXiv  Detail & Related papers  (2023-07-25T14:20:52Z)
• Multi-Grained Multimodal Interaction Network for Entity Linking [65.30260033700338]
  Multimodal entity linking task aims at resolving ambiguous mentions to a multimodal knowledge graph. We propose a novel Multi-GraIned Multimodal InteraCtion Network $textbf(MIMIC)$ framework for solving the MEL task.
  arXiv  Detail & Related papers  (2023-07-19T02:11:19Z)
• Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models [53.31917090073727]
  We propose a neural network-based emotion recognition framework that uses a late fusion of transfer-learned and fine-tuned models from speech and text modalities. We evaluate the effectiveness of our proposed multimodal approach on the interactive emotional dyadic motion capture dataset.
  arXiv  Detail & Related papers  (2022-02-16T00:23:42Z)
• VIRT: Improving Representation-based Models for Text Matching through Virtual Interaction [50.986371459817256]
  We propose a novel textitVirtual InteRacTion mechanism, termed as VIRT, to enable full and deep interaction modeling in representation-based models. VIRT asks representation-based encoders to conduct virtual interactions to mimic the behaviors as interaction-based models do.
  arXiv  Detail & Related papers  (2021-12-08T09:49:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.