M3HG：面向对话中情绪原因三元组抽取的多模态、多尺度、多类型节点异质图
M3HG: Multimodal, Multi-scale, and Multi-type Node Heterogeneous Graph for Emotion Cause Triplet Extraction in Conversations
AI总结：
本文提出M3HG模型，通过多模态异质图显式建模情绪与因果上下文，并融合话语内外部信息，以解决多模态对话中情绪原因三元组抽取问题，同时引入首个多场景数据集MECAD，实验验证其有效性。
摘要：
多模态对话中的情绪原因三元组抽取（MECTEC）近期在社交媒体分析中受到广泛关注，旨在同时抽取情绪话语、原因话语和情绪类别。然而，相关数据集的稀缺（仅有一个已发布的数据集，且对话场景高度单一）阻碍了该领域的模型发展。为解决这一问题，我们引入了MECAD，这是首个多模态、多场景的MECTEC数据集，包含来自56部电视剧的989段对话，覆盖了广泛的对话情境。此外，现有的MECTEC方法未能显式建模情绪和因果上下文，且忽略了不同层级语义信息的融合，导致性能下降。在本文中，我们提出了M3HG，一种新颖的模型，通过多模态异质图显式捕获情绪和因果上下文，并在话语间和话语内两个层级有效融合上下文信息。大量实验表明，与现有最先进方法相比，M3HG具有有效性。代码和数据集可在https://github.com/redifinition/M3HG获取。
英文摘要：
Emotion Cause Triplet Extraction in Multimodal Conversations (MECTEC) has recently gained significant attention in social media analysis, aiming to extract emotion utterances, cause utterances, and emotion categories simultaneously. However, the scarcity of related datasets, with only one published dataset featuring highly uniform dialogue scenarios, hinders model development in this field. To address this, we introduce MECAD, the first multimodal, multi-scenario MECTEC dataset, comprising 989 conversations from 56 TV series spanning a wide range of dialogue contexts. In addition, existing MECTEC methods fail to explicitly model emotional and causal contexts and neglect the fusion of semantic information at different levels, leading to performance degradation. In this paper, we propose M3HG, a novel model that explicitly captures emotional and causal contexts and effectively fuses contextual information at both inter- and intra-utterance levels via a multimodal heterogeneous graph. Extensive experiments demonstrate the effectiveness of M3HG compared with existing state-of-the-art methods. The codes and dataset are available at https://github.com/redifinition/M3HG.
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。
