多方对话中的情绪翻转推理
Emotion Flip Reasoning in Multiparty Conversations
AI总结：
本文探索多方对话中基于诱发因素的情绪翻转推理（EFR）任务，构建了MELD-I数据集并提出TGIF神经架构，通过Transformer编码器和堆叠GRU捕获对话上下文与情绪动态，在F1分数上提升4-12%，并验证了零样本泛化能力。
摘要：
在对话中，说话者可能具有不同的情绪状态，其动态变化在理解对话的情绪语篇中起着重要作用。然而，仅仅检测情绪不足以完全理解对话过程中说话者特定情绪的变化。为了高效理解说话者的情绪动态，必须识别说话者所表达的情绪变化或翻转背后的原因或诱发因素。在本文中，我们探索了名为基于诱发因素的情绪翻转推理（EFR）的任务，旨在识别对话中说话者情绪翻转背后的诱发因素。例如，从喜悦到愤怒的情绪翻转可能由威胁等诱发因素引起。为促进该任务，我们提出了MELD-I数据集，包含符合情绪心理学的真实EFR诱发因素标签。为评估该数据集，我们提出了一种名为TGIF的新型神经架构，利用Transformer编码器和堆叠GRU来捕获对话中的对话上下文、说话者动态和情绪序列。我们的评估表明，与用于该任务的五个基线相比，取得了最先进的性能（F1分数提高4-12%）。此外，我们在零样本设置下确立了TGIF在未见数据集上的泛化能力。另外，我们提供了竞争模型的详细分析，突出了我们神经架构的优势和局限性。
英文摘要：
In a conversational dialogue, speakers may have different emotional states and their dynamics play an important role in understanding dialogue's emotional discourse. However, simply detecting emotions is not sufficient to entirely comprehend the speaker-specific changes in emotion that occur during a conversation. To understand the emotional dynamics of speakers in an efficient manner, it is imperative to identify the rationale or instigator behind any changes or flips in emotion expressed by the speaker. In this paper, we explore the task called Instigator based Emotion Flip Reasoning (EFR), which aims to identify the instigator behind a speaker's emotion flip within a conversation. For example, an emotion flip from joy to anger could be caused by an instigator like threat. To facilitate this task, we present MELD-I, a dataset that includes ground-truth EFR instigator labels, which are in line with emotional psychology. To evaluate the dataset, we propose a novel neural architecture called TGIF, which leverages Transformer encoders and stacked GRUs to capture the dialogue context, speaker dynamics, and emotion sequence in a conversation. Our evaluation demonstrates state-of-the-art performance (+4-12% increase in F1-score) against five baselines used for the task. Further, we establish the generalizability of TGIF on an unseen dataset in a zero-shot setting. Additionally, we provide a detailed analysis of the competing models, highlighting the advantages and limitations of our neural architecture.
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。
