Existing multimodal sentiment analysis (MSA) methods have achieved significant success, leveraging cross-modal large-scale models (LLMs) and extensive pre-training data. However, these methods struggle to handle MSA tasks in low-resource languages. While multilingual LLMs enable cross-lingual transfer, they are limited to textual data and cannot address multimodal scenarios. To achieve MSA in low-resource languages, we propose a novel transfer learning framework named Language Family Disentanglement and Rethinking Transfer (LFD-RT). During pre-training, we establish cross-lingual and cross-modal alignments, followed by a language family disentanglement module that enhances the sharing of language universals within families while reducing noise from cross-family alignments. We propose a rethinking strategy for unsupervised fine-tuning that adapts the pre-trained model to MSA tasks in low-resource languages. Experimental results demonstrate the superiority of our method and its strong language-transfer capability on target low-resource languages. We commit to making our code and data publicly available, and the access link will be provided here.

Legal question answering (LQA) aims to bridge the gap between the limited availability of legal professionals and the high demand for legal assistance. Traditional LQA approaches typically either select the optimal answers from an answer set or extract answers from law texts. However, they often struggle to provide relevant answers to complex, real-world questions due to the rigidity of predetermined answers. Although recent advancements in legal large language models have shown some potential in enhancing answer relevance, they fail to address the multiple user-specific circumstances, i.e., factual details in questions. To address these issues, we (1) construct the first publicly available legal community question-answering (LegalCQA) dataset; and (2) propose a Hierarchical Legal Knowledge Integration (H-LegalKI) framework. LegalCQA is collected from two widely used legal forums for developing user-centered LQA models. For H-LegalKI, we design a legal knowledge retriever that gathers comprehensive legal knowledge based on both entire questions and individual sentences. And an answer generation model is designed to understand question- and sentence-level factual details and integrate corresponding legal knowledge in a hierarchical way. Additionally, we design a de-redundancy module to remove redundant legal knowledge. Experiments on LegalCQA demonstrate the superiority of our framework over competitive baselines.