Domain adaptation is widely employed in cross-domain sentiment analysis, enabling the transfer of models from label-rich source domains to target domain with fewer or no labels. However, concerns have been raised regarding their robustness and sensitivity to data distribution shift, particularly when encountering significant disparities in data distribution between the different domains. To tackle this problem, we introduce a framework CDAˆ2 for cross-domain adaptation in low-resource sentiment analysis, which utilizes counterfactual diffusion augmentation. Specifically, it employs samples derived from domain-relevant word substitutions in source domain samples to guide the diffusion model for generating high-quality counterfactual target domain samples. We adopt a soft absorbing state and MMD loss during the training stage, and use advanced ODE solvers to expedite the sampling process. Our experiments demonstrate that CDAˆ2 generates high-quality target samples and achieves state-of-the-art performance in cross-domain sentiment analysis.

The text-based compliance checking aims to verify whether a company’s business processes comply with laws, regulations, and industry standards using NLP techniques. Existing methods can be divided into two categories: Logic-based methods offer the advantage of precise and reliable reasoning processes but lack flexibility. Semantic embedding methods are more generalizable; however, they may lose structured information and lack logical coherence. To combine the strengths of both approaches, we propose a compliance checking framework based on Retrieval-Augmented Generation (RAG). This framework includes a static layer for storing factual knowledge, a dynamic layer for storing regulatory and business process information, and a computational layer for retrieval and reasoning. We employ an eventic graph to structurally describe regulatory information as we recognize that the knowledge in regulatory documents is centered not on entities but on actions and states. We conducted experiments on Chinese and English compliance checking datasets. The results demonstrate that our framework consistently achieves state-of-the-art results across various scenarios, surpassing other baselines.