Emotional support conversation (ESC) task aims to relieve the emotional distress of users who have high-intensity of negative emotions. However, due to the ignorance of emotion intensity modelling which is essential for ESC, previous methods fail to capture the transition of emotion intensity effectively. To this end, we propose a Multi-stream information Fusion Framework (MFF-ESC) to thoroughly fuse three streams (text semantics stream, emotion intensity stream, and feedback stream) for the modelling of emotion intensity, based on a designed multi-stream fusion unit. As the difficulty of modelling subtle transitions of emotion intensity and the strong emotion intensity-feedback correlations, we use the KL divergence between feedback distribution and emotion intensity distribution to further guide the learning of emotion intensities. Experimental results on automatic and human evaluations indicate the effectiveness of our method.

Extracting generalized and robust representations is a major challenge in emotion recognition in conversations (ERC).To address this, we propose a supervised adversarial contrastive learning (SACL) framework for learning classspread structured representations in a supervised manner.SACL applies contrast-aware adversarial training to generate worst-case samples and uses joint class-spread contrastive learning to extract structured representations.It can effectively utilize label-level feature consistency and retain fine-grained intra-class features.To avoid the negative impact of adversarial perturbations on context-dependent data, we design a contextual adversarial training (CAT) strategy to learn more diverse features from context and enhance the model's context robustness.Under the framework with CAT, we develop a sequence-based SACL-LSTM to learn label-consistent and context-robust features for ERC.Experiments on three datasets show that SACL-LSTM achieves state-of-the-art performance on ERC.Extended experiments prove the effectiveness of SACL and CAT.

The emotion recognition in conversation (ERC) task aims to predict the emotion label of an utterance in a conversation. Since the dependencies between speakers are complex and dynamic, which consist of intra- and inter-speaker dependencies, the modeling of speaker-specific information is a vital role in ERC. Although existing researchers have proposed various methods of speaker interaction modeling, they cannot explore dynamic intra- and inter-speaker dependencies jointly, leading to the insufficient comprehension of context and further hindering emotion prediction. To this end, we design a novel speaker modeling scheme that explores intra- and inter-speaker dependencies jointly in a dynamic manner. Besides, we propose a Speaker-Guided Encoder-Decoder (SGED) framework for ERC, which fully exploits speaker information for the decoding of emotion. We use different existing methods as the conversational context encoder of our framework, showing the high scalability and flexibility of the proposed framework. Experimental results demonstrate the superiority and effectiveness of SGED.