Multimodal large language models (MLLMs) have attracted considerable attention for their impressive capabilities in understanding and generating visual-language content, particularly in tasks such as visual question answering (VQA). However, the rapid evolution of knowledge in real-world applications poses challenges for these models: offline training becomes increasingly costly, and exposure to non-stationary data streams often leads to catastrophic forgetting. In this paper, we propose CL-MoE+, a dual-momentum Mixture-of-Experts (MoE) framework based on MLLMs for continual VQA. Our method integrates continual learning into MLLMs to leverage the rich commonsense knowledge embedded in large language models.We introduce a Dual-Router MoE (RMoE) module that selects both global and local experts through task-level and instance-level routers, enabling robust and context-aware expert allocation. Furthermore, we design an adaptive Momentum MoE (MMoE) to update experts’ parameters based on the knowledge drift degree and their relevance to specific tasks, thereby facilitating knowledge integration without forgetting. Extensive experiments on a 10-task split of the VQA v2 benchmark demonstrate that CL-MoE+ achieves state-of-the-art performance, validating its effectiveness in both retaining historical knowledge and learning new information in the continual learning setting.

Multimodal large language models (MLLMs) have garnered widespread attention from researchers due to their remarkable understanding and generation capabilities in visual language tasks (e.g., visual question answering). However, the rapid pace of knowledge updates in the real world makes offline training of MLLMs costly, and when faced with non-stationary data streams, MLLMs suffer from catastrophic forgetting during learning. In this paper, we propose an MLLMs-based dual momentum Mixture-of-Experts (CL-MoE) framework for continual visual question answering (VQA). We integrate MLLMs with continual learning to utilize the rich commonsense knowledge in LLMs. We introduce a Dual-Router MoE (RMoE) strategy to select the global and local experts using task-level and instance-level routers, to robustly assign weights to the experts most appropriate for the task. Then, we design a dynamic Momentum MoE (MMoE) to update the parameters of experts dynamically based on the relationships between the experts and tasks/instances, so that the model can absorb new knowledge while maintaining existing knowledge. The extensive experimental results indicate that our method achieves state-of-the-art performance on 10 VQA tasks, proving the effectiveness of our approach.

Aspect-Based Sentiment Analysis (ABSA) stands as a crucial task in predicting the sentiment polarity associated with identified aspects within text. However, a notable challenge in ABSA lies in precisely determining the aspects’ boundaries (start and end indices), especially for long ones, due to users’ colloquial expressions. We propose DiffusionABSA, a novel diffusion model tailored for ABSA, which extracts the aspects progressively step by step. Particularly, DiffusionABSA gradually adds noise to the aspect terms in the training process, subsequently learning a denoising process that progressively restores these terms in a reverse manner. To estimate the boundaries, we design a denoising neural network enhanced by a syntax-aware temporal attention mechanism to chronologically capture the interplay between aspects and surrounding text. Empirical evaluations conducted on eight benchmark datasets underscore the compelling advantages offered by DiffusionABSA when compared against robust baseline models. Our code is publicly available at https://github.com/Qlb6x/DiffusionABSA.

Zero-shot text classification (ZSTC) poses a big challenge due to the lack of labeled data for unseen classes during training. Most studies focus on transferring knowledge from seen classes to unseen classes, which have achieved good performance in most cases. Whereas, it is difficult to transfer knowledge when the classes have semantic gaps or low similarities. In this paper, we propose a prompt-based method, which enhances semantic understanding for each class and learns the matching between texts and classes for better ZSTC. Specifically, we first generate discriminative words for class description with prompt inserting (PIN). Then, a prompt matching (POM) model is learned to determine whether the text can well match the class description. Experiments on three benchmark datasets show the great advantages of our proposed method. In particular, we achieve the state-of-the-art performance on the unseen classes, while maintaining comparable strength with the existing ZSTC approaches regarding to the seen classes.

Structured sentiment analysis, which aims to extract the complex semantic structures such as holders, expressions, targets, and polarities, has obtained widespread attention from both industry and academia. Unfortunately, the existing structured sentiment analysis datasets refer to a few languages and are relatively small, limiting neural network models' performance. In this paper, we focus on the cross-lingual structured sentiment analysis task, which aims to transfer the knowledge from the source language to the target one. Notably, we propose a Knowledge-Enhanced Adversarial Model (KEAM) with both implicit distributed and explicit structural knowledge to enhance the cross-lingual transfer. First, we design an adversarial embedding adapter for learning an informative and robust representation by capturing implicit semantic information from diverse multi-lingual embeddings adaptively. Then, we propose a syntax GCN encoder to transfer the explicit semantic information (e.g., universal dependency tree) among multiple languages. We conduct experiments on five datasets and compare KEAM with both the supervised and unsupervised methods. The extensive experimental results show that our KEAM model outperforms all the unsupervised baselines in various metrics.

Previous studies about event-level sentiment analysis (SA) usually model the event as a topic, a category or target terms, while the structured arguments (e.g., subject, object, time and location) that have potential effects on the sentiment are not well studied. In this paper, we redefine the task as structured event-level SA and propose an End-to-End Event-level Sentiment Analysis (E3SA) approach to solve this issue. Specifically, we explicitly extract and model the event structure information for enhancing event-level SA. Extensive experiments demonstrate the great advantages of our proposed approach over the state-of-the-art methods. Noting the lack of the dataset, we also release a large-scale real-world dataset with event arguments and sentiment labelling for promoting more researches.