ECRC: Emotion-Causality Recognition in Korean Conversation for GCN

• URL: http://arxiv.org/abs/2403.10764v1
• Date: Sat, 16 Mar 2024 02:07:31 GMT
• Title: ECRC: Emotion-Causality Recognition in Korean Conversation for GCN
• Authors: J. K. Lee, T. M. Chung,
• Abstract summary: We propose the emotion-causality recognition in conversation (ECRC) model, which is based on a novel graph structure. In this study, we overcome the limitations of previous embeddings by utilizing both word- and sentence-level embeddings. This model uniquely integrates the bidirectional long short-term memory (Bi-LSTM) and graph neural network (GCN) models for Korean conversation analysis.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this multi-task learning study on simultaneous analysis of emotions and their underlying causes in conversational contexts, deep neural network methods were employed to effectively process and train large labeled datasets. However, these approaches are typically limited to conducting context analyses across the entire corpus because they rely on one of the two methods: word- or sentence-level embedding. The former struggles with polysemy and homonyms, whereas the latter causes information loss when processing long sentences. In this study, we overcome the limitations of previous embeddings by utilizing both word- and sentence-level embeddings. Furthermore, we propose the emotion-causality recognition in conversation (ECRC) model, which is based on a novel graph structure, thereby leveraging the strengths of both embedding methods. This model uniquely integrates the bidirectional long short-term memory (Bi-LSTM) and graph neural network (GCN) models for Korean conversation analysis. Compared with models that rely solely on one embedding method, the proposed model effectively structures abstract concepts, such as language features and relationships, thereby minimizing information loss. To assess model performance, we compared the multi-task learning results of three deep neural network models with varying graph structures. Additionally, we evaluated the proposed model using Korean and English datasets. The experimental results show that the proposed model performs better in emotion and causality multi-task learning (74.62% and 75.30%, respectively) when node and edge characteristics are incorporated into the graph structure. Similar results were recorded for the Korean ECC and Wellness datasets (74.62% and 73.44%, respectively) with 71.35% on the IEMOCAP English dataset.

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