Zero-shot multitask intent and emotion prediction from multimodal data: A benchmark study

Singh, Gopendra Vikram; Firdaus, Mauajama; Chauhan, Dushyant Singh; Ekbal, Asif; Bhattacharyya, Pushpak

doi:10.1016/j.neucom.2023.127128

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2024

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Cited by 3 publications

References 37 publications

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Cascading Global and Sequential Temporal Representations with Local Context Modeling for EEG-Based Emotion Recognition

Kang,

Choi,

Kim

2024

Lecture Notes in Computer Science

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Cascading Global and Sequential Temporal Representations with Local Context Modeling for EEG-Based Emotion Recognition

Kang,

Choi,

Kim

2024

Lecture Notes in Computer Science

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Modality Weights Based Fusion Model for Social Perception Prediction in Video, Audio, and Text

Lim,

Yang,

Yang

et al. 2024

Proceedings of the 5th on Multimodal Sentiment Analysis Challenge and Workshop: Social Perception and Humor

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Emotion inference in conversations based on commonsense enhancement and graph structures

Zhang,

Xu,

Xie

et al. 2024

PLoS ONE

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In the task of emotion inference, a common issue is the lack of common sense knowledge, particularly in the context of dialogue, where traditional research has failed to effectively extract structural features, resulting in lower accuracy in emotion inference. To address this, this paper proposes a dialogue emotion inference model based on Common Sense Enhancement and Graph Model (CEICG). This model integrates external common sense with graph model techniques by dynamically constructing nodes and defining diverse edge relations to simulate the evolution of dialogue, thereby effectively capturing the structural and semantic features of the conversation. The model employs two methods to incorporate external common sense into the graph model, overcoming the limitations of previous models in understanding complex dialogue structures and the absence of external knowledge. This strategy of integrating external common sense significantly enhances the model’s emotion inference capabilities, improving the understanding of emotions in dialogue. Experimental results demonstrate that the CEICG model outperforms six existing baseline models in emotion inference tasks across three datasets.

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Zero-shot multitask intent and emotion prediction from multimodal data: A benchmark study

Cited by 3 publications

References 37 publications

Cascading Global and Sequential Temporal Representations with Local Context Modeling for EEG-Based Emotion Recognition

Cascading Global and Sequential Temporal Representations with Local Context Modeling for EEG-Based Emotion Recognition

Modality Weights Based Fusion Model for Social Perception Prediction in Video, Audio, and Text

Emotion inference in conversations based on commonsense enhancement and graph structures

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