SI-LSTM: Speaker Hybrid Long-short Term Memory and Cross Modal Attention for Emotion Recognition in Conversation

• URL: http://arxiv.org/abs/2305.03506v3
• Date: Tue, 6 Jun 2023 12:19:35 GMT
• Title: SI-LSTM: Speaker Hybrid Long-short Term Memory and Cross Modal Attention for Emotion Recognition in Conversation
• Authors: Xingwei Liang, You Zou, Ruifeng Xu
• Abstract summary: Emotion Recognition in Conversation(ERC) 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.
• Score: 16.505046191280634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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