COSMIC: COmmonSense knowledge for eMotion Identification in Conversations (2010.02795v1)

Abstract: In this paper, we address the task of utterance level emotion recognition in conversations using commonsense knowledge. We propose COSMIC, a new framework that incorporates different elements of commonsense such as mental states, events, and causal relations, and build upon them to learn interactions between interlocutors participating in a conversation. Current state-of-the-art methods often encounter difficulties in context propagation, emotion shift detection, and differentiating between related emotion classes. By learning distinct commonsense representations, COSMIC addresses these challenges and achieves new state-of-the-art results for emotion recognition on four different benchmark conversational datasets. Our code is available at https://github.com/declare-lab/conv-emotion.

• The paper introduces a novel framework called COSMIC that integrates commonsense knowledge and deep learning for enhanced emotion recognition in dialogues.
• It employs a three-stage methodology using RoBERTa, COMET, and GRU networks to effectively model complex contextual and emotional cues.
• Experiments on multiple datasets demonstrate COSMIC's superior performance over existing models in understanding nuanced emotions in conversations.

Overview of the COSMIC Framework for Emotion Recognition in Conversations

This paper presents a novel approach to emotion recognition in conversations through the use of commonsense knowledge, named the COSMIC framework. Unlike conventional methods which face challenges related to context propagation and differentiating between closely related emotions, COSMIC incorporates various elements of commonsense knowledge, including mental states and causal relations, to enhance conversational understanding.

Methodology

The COSMIC framework is structured around three core stages:

1. Context-Independent Feature Extraction: Utilizes the RoBERTa model to extract initial feature vectors from conversational utterances.
2. Commonsense Feature Extraction: Employs the COMET model, trained on the ATOMIC knowledge graph, to generate continuous vectors representing commonsense elements such as intent and reactions of both speakers and listeners.
3. Commonsense Incorporation: Integrates these commonsense features into the conversation model using a series of GRU networks to update internal, external, intent, and emotion states for emotion classification.

Datasets and Experimental Setup

The framework was evaluated on four benchmark datasets: IEMOCAP, MELD, DailyDialog, and EmoryNLP, each consisting of dialogues with multi-party interactions and emotions labeled across various categories. The implementation demonstrated superior performance, achieving state-of-the-art results by incorporating deeper contextual and inferential understanding.

Results and Analysis

COSMIC showed marked improvement over existing models such as DialogueRNN and transformer-based methods. Notably, the framework effectively handled the complex emotional dynamics in datasets with diverse conversational flows, achieving higher accuracy on datasets like MELD and EmoryNLP where contextual dependencies are crucial.

The ablation studies confirmed the significant impact of commonsense knowledge, particularly speaker-specific features, in improving emotion recognition accuracy. By modeling intent and reactions more effectively, COSMIC addressed issues like emotion shift detection and misclassification among similar emotions.

Practical and Theoretical Implications

The COSMIC framework advances practical applications in emotion-aware conversational agents, enhancing their ability to understand and respond to nuanced emotional expressions. Theoretically, the integration of commonsense knowledge sets a precedent for future research in AI, promoting models that can reason and interact in more human-like ways.

Future Directions

While COSMIC represents a significant step forward, future research may explore more robust and expansive commonsense knowledge sources, including multi-modal inputs, to further refine emotion recognition models. Advances in this area could lead to more empathetic and contextually aware AI systems, expanding their application in areas such as mental health, customer service, and beyond.

In conclusion, COSMIC exemplifies a progressive approach to analyzing conversations by embedding commonsense reasoning within computational frameworks, enhancing both conversational understanding and emotional depth in AI systems.