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.

本研究使用深度神经网络方法进行情感和对话背后原因的多任务学习，并利用词语和句子级嵌入来克服以往方法的限制。提出的情感 - 因果识别对话模型独特地结合了双向长短期记忆和图神经网络模型，有效地构建抽象概念并最小化信息损失。实验结果表明，该模型在情感和因果多任务学习上表现更好，并且结合节点和边特征的图结构进一步提升性能。