SI-LSTM：用于对话情感识别的说话人混合长短期记忆与跨模态注意力
SI-LSTM: Speaker Hybrid Long-short Term Memory and Cross Modal Attention for Emotion Recognition in Conversation
AI总结：
针对对话情感识别中说话人状态追踪与多模态交互建模不足的问题，提出SI-LSTM循环结构并结合跨模态注意力，在两个基准数据集上优于现有基线。
摘要：
跨模态对话情感识别（Emotion Recognition in Conversation, ERC）对多种应用至关重要，包括智能医疗、对话人工智能以及聊天历史中的观点挖掘。ERC的关键在于对整个对话中的跨模态和跨时间交互进行建模。以往方法在学习对话的时间序列信息方面取得了进展，但缺乏追踪对话中每位说话人不同情感状态的能力。本文针对ERC任务提出一种名为说话人信息增强长短期记忆（Speaker Information Enhanced Long-Short Term Memory, SI-LSTM）的循环结构，在该结构中，不同说话人的情感状态能够以顺序方式被追踪，从而增强对对话中情感的学习。此外，为改进ERC中多模态特征的学习，我们利用跨模态注意力组件融合不同模态之间的特征，并对来自不同模态的重要信息之间的交互进行建模。在两个基准数据集上的实验结果表明，在多模态数据的ERC任务中，所提出的SI-LSTM相较于最先进的基线方法具有优越性。
英文摘要：
Emotion Recognition in Conversation~(ERC) across modalities is of vital importance for a variety of applications, including intelligent healthcare, artificial intelligence for conversation, and opinion mining over chat history. The crux of ERC is to model both cross-modality and cross-time interactions throughout the conversation. Previous methods have made progress in learning the time series information of conversation while lacking the ability to trace down the different emotional states of each speaker in a conversation. In this paper, we propose a recurrent structure called Speaker Information Enhanced Long-Short Term Memory (SI-LSTM) for the ERC task, where the emotional states of the distinct speaker can be tracked in a sequential way to enhance the learning of the emotion in conversation. Further, to improve the learning of multimodal features in ERC, we utilize a cross-modal attention component to fuse the features between different modalities and model the interaction of the important information from different modalities. Experimental results on two benchmark datasets demonstrate the superiority of the proposed SI-LSTM against the state-of-the-art baseline methods in the ERC task on multimodal data.
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。
