Emotion recognition in conversation (ERC) involves identifying emotional labels associated with utterances within a conversation, a task that is essential for developing empathetic robots. Current research emphasizes contextual factors, the speaker’s influence, and extracting complementary information across different modalities. However, it often overlooks the cross-modal noise at the semantic level and the redundant information brought by the features themselves. This study introduces a diffusion-based approach designed to effectively address the challenges posed by redundant information and unexpected noise while robustly capturing shared semantics, thus facilitating the learning of compact and representative features from multimodal data. Specifically, we present the Multi-Condition Guided Diffusion Network (McDiff). McDiff employs a modal prior knowledge extraction strategy to derive the prior distribution for each modality, thereby enhancing the regional attention of each modality and applying the generated prior distribution at each diffusion step. Furthermore, we propose a method to learn the mutual information of each modality through a specific objective constraints approach prior to the forward process, which aims to improve inter-modal interaction and mitigate the effects of noise and redundancy. Comprehensive experiments conducted on two multimodal datasets, IEMOCAP and MELD, demonstrate that McDiff significantly surpasses existing state-of-the-art methodologies, thereby affirming the generalizability and efficacy of the proposed model.

Text-to-SQL is a task with excellent prospects and challenges, and it aims to convert natural language queries (NL) into corresponding structured query language (SQL) statements. The main challenge of this task is how to efficiently transform unstructured data and structured data. In recent years, the emergence of large language models (LLMs) has further promoted the development of this field. However, current LLM-based text-to-SQL methods rely on specific few-shot example construction, resulting in poor performance across domains. To solve this problem, we propose a text-to-SQL method of self-contrastive loop of thought structure. This method designs the LLM inference process as a loop structure based on the comparison of positive and negative examples. The model optimizes the generated results through continuous verification and error correction, greatly improving accuracy and reducing dependence on few-shot example construction. The experimental results on SPIDER and BIRD datasets show that this method can generate SQL with higher precision without relying on few-shot example construction.