Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations (1909.10681v2)

Abstract: Messages in human conversations inherently convey emotions. The task of detecting emotions in textual conversations leads to a wide range of applications such as opinion mining in social networks. However, enabling machines to analyze emotions in conversations is challenging, partly because humans often rely on the context and commonsense knowledge to express emotions. In this paper, we address these challenges by proposing a Knowledge-Enriched Transformer (KET), where contextual utterances are interpreted using hierarchical self-attention and external commonsense knowledge is dynamically leveraged using a context-aware affective graph attention mechanism. Experiments on multiple textual conversation datasets demonstrate that both context and commonsense knowledge are consistently beneficial to the emotion detection performance. In addition, the experimental results show that our KET model outperforms the state-of-the-art models on most of the tested datasets in F1 score.

• The paper presents a novel transformer architecture that integrates hierarchical self-attention with external commonsense knowledge to enhance emotion detection in conversations.
• It employs a context-aware affective graph attention mechanism that dynamically merges inputs from ConceptNet and NRC_VAD for improved sentiment sensitivity.
• Experimental results demonstrate that KET consistently outperforms state-of-the-art models on diverse datasets, advancing the field of affective computing.

Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations

The paper "Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations" proposes a novel approach for detecting emotions in textual conversations, addressing the inherent challenges posed by the need to consider both the context and the commonsense knowledge that humans naturally employ to express and understand emotions. The authors introduce the Knowledge-Enriched Transformer (KET), an innovative model which leverages hierarchical self-attention and integrates external commonsense knowledge via a context-aware affective graph attention mechanism. This model showcases improvements over existing methodologies by simultaneously considering contextual and external knowledge bases.

Key Contributions and Methodology

Among the paper's significant contributions is the novel use of a Transformer architecture for emotion detection in conversations. By employing a hierarchical self-attention mechanism, the authors address the structural nature of conversations, allowing the model to effectively capture long-range dependencies within the text. This contrasts with traditional gates and CNN-based methods that might suffer from inefficiencies when modeling sequences of this length. In particular, the model is designed with separate encoder and decoder structures to deal with context and response, differentiating it from models such as BERT that treat conversations as monolithic text blocks.

Another prominent feature of KET is its dynamic integration of external knowledge sources. The authors pull data from ConceptNet and NRC_VAD, leveraging a context-aware affective graph attention mechanism to dynamically adjust the model’s sensitivity to the relevant emotional and sentiment-related concepts associated with input terms. This advancement addresses a noted gap in the literature concerning the integration of commonsense knowledge in deep learning models for emotion detection.

Experimental Results

The performance enhancements demonstrated by KET were validated across multiple datasets of varying domains and sizes, such as conversational data from social media platforms and scripted dialogue from TV shows. On these datasets, KET consistently outperformed several state-of-the-art models, such as CNN+cLSTM and DialogueRNN, particularly in terms of F1 scores in detecting emotions like happiness, sadness, and anger. The experiments underscored the importance of both the context and external knowledge in improving emotion detection capabilities.

Discussion and Implications

The methodology employed by KET highlights an important trajectory in the development of AI models that require a deep understanding of nuanced human emotions. By incorporating commonsense knowledge into the architecture, KET addresses a core challenge of emotion detection: the implicit and context-heavy nature of emotional expression.

The findings suggest that future developments in AI could benefit significantly from this approach, particularly as more extensive and comprehensive knowledge graphs and emotion lexicons become available. This potential indicates further research opportunities in multilingual settings and adaptation to other domains of conversation analysis.

In conclusion, the paper provides robust evidence of the utility of combining hierarchical modeling techniques with integrated commonsense knowledge in emotion detection tasks. Such advancements not only provide practical enhancements for emotion analysis in real-time conversational agents but also contribute to the theoretical understanding of affective computing, marking an essential step towards more emotionally intelligent AI systems.