DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations (2106.01978v2)

Abstract: Emotion Recognition in Conversations (ERC) has gained increasing attention for developing empathetic machines. Recently, many approaches have been devoted to perceiving conversational context by deep learning models. However, these approaches are insufficient in understanding the context due to lacking the ability to extract and integrate emotional clues. In this work, we propose novel Contextual Reasoning Networks (DialogueCRN) to fully understand the conversational context from a cognitive perspective. Inspired by the Cognitive Theory of Emotion, we design multi-turn reasoning modules to extract and integrate emotional clues. The reasoning module iteratively performs an intuitive retrieving process and a conscious reasoning process, which imitates human unique cognitive thinking. Extensive experiments on three public benchmark datasets demonstrate the effectiveness and superiority of the proposed model.

• The paper introduces a cognitive-inspired, two-phase model combining perceptive and cognitive processes for context-aware emotion prediction.
• It achieves significant gains, including a 3.2% accuracy boost on IEMOCAP and an 11.1% reduction in MAE on SEMAINE.
• The innovative multi-turn reasoning and attention mechanisms demonstrate the effectiveness of integrating cognitive theories in conversational AI.

Contextual Reasoning Networks for Emotion Recognition in Conversations: An Expert Analysis

The task of Emotion Recognition in Conversations (ERC) has become increasingly pertinent in the development of empathetic machines across various domains, such as social opinion mining, intelligent assistance, and healthcare. The paper "DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations" introduces DialogueCRN, a model designed to address limitations in current ERC systems, which often fail to adequately extract and integrate the emotional cues embedded within conversational context.

Key Contributions and Methodology

DialogueCRN differentiates itself by employing a two-fold approach inspired by the Cognitive Theory of Emotion. This involves a perceptive phase and a cognitive phase that together enhance the model's understanding of conversational nuances. The perceptive phase utilizes Long Short-Term Memory (LSTM) networks to capture both situation-level and speaker-level context. By encoding these contextual features into global memory representations, DialogueCRN establishes the groundwork for further cognitive processing.

The innovation primarily lies in the cognitive phase, which comprises multi-turn reasoning modules. These modules mimic human cognitive processes by iteratively executing an intuitive retrieving process and a conscious reasoning process. The intuitive retrieving process leverages attention mechanisms to extract emotional cues from static global memories, whereas the conscious reasoning process employs LSTMs to discern the intrinsic logical order of these cues, thereby enabling the integration of dynamic contextual information. The model ultimately predicts the emotion of an utterance by combining these contextual cues through an emotion classifier.

Experimental Evaluation

The proposed model's performance was validated against three benchmark datasets: IEMOCAP, SEMAINE, and MELD. DialogueCRN demonstrated significant improvements over several state-of-the-art methods, including TextCNN, Memnet, and DialogueGCN. Notably, it achieved accuracy improvements of 3.2% on the IEMOCAP dataset and delivered a 11.1% reduction in Mean Absolute Error for the Arousal attribute on the SEMAINE dataset. This underscores the model's ability to effectively extract and integrate contextual emotional information.

Implications and Future Research

The findings of this paper have notable implications for the field of conversational AI, particularly in enhancing the emotional intelligence of machines. By incorporating cognition-inspired reasoning, DialogueCRN not only advances methodologies in ERC but also offers insights into the broader integration of cognitive theories within machine learning frameworks. Future research could explore the generalization of cognitive processing techniques beyond ERC, potentially applying them to other tasks in natural language understanding and generation.

Conclusion

DialogueCRN represents a substantial step forward in the ERC landscape by addressing the critical challenge of emotional clue integration. Through a blend of cognitive-inspired reasoning and advanced neural architectures, it offers a robust framework for understanding and predicting emotions in conversations. The advancements presented by DialogueCRN open avenues for continued exploration into the application of cognitive sciences in AI research, with the potential to significantly impact the development of more humane and emotionally aware computational systems.