ECRC：用于GCN的韩语对话情感-因果关系识别
ECRC: Emotion-Causality Recognition in Korean Conversation for GCN
AI总结：
本研究提出ECRC模型，结合词级与句子级嵌入及Bi-LSTM和GCN，用于韩语对话中的情感与因果关系多任务识别，在多个数据集上取得更优性能。
摘要：
在这项关于对话情境中情感及其潜在原因同步分析的多任务学习研究中，采用了深度神经网络方法以有效处理并训练大型标注数据集。然而，这些方法通常仅限于对整个语料库进行上下文分析，因为它们依赖于两种方法之一：词级或句子级嵌入。前者难以处理多义词和同音异义词，而后者在处理长句时会导致信息丢失。在本研究中，我们通过同时利用词级和句子级嵌入来克服先前嵌入的局限性。此外，我们提出了基于新颖图结构的情感-因果关系识别（ECRC）模型，从而充分利用了这两种嵌入方法的优势。该模型独特地集成了双向长短期记忆（Bi-LSTM）和图神经网络（GCN）模型用于韩语对话分析。与仅依赖一种嵌入方法的模型相比，所提出的模型有效地结构化语言特征和关系等抽象概念，从而最小化信息丢失。为了评估模型性能，我们比较了三种具有不同图结构的深度神经网络模型的多任务学习结果。此外，我们使用韩语和英语数据集评估了所提出的模型。实验结果表明，当节点和边特征被纳入图结构时，所提出的模型在情感和因果关系多任务学习（分别为74.62%和75.30%）中表现更好。在韩语ECC和Wellness数据集上记录了类似结果（分别为74.62%和73.44%），在IEMOCAP英语数据集上为71.35%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
