Stance detection is a fundamental task in Natural Language Processing (NLP). It is challenging due to diverse expressions and topics related to the targets from multiple domains. Recently, prompt-tuning has been introduced to convert the original task into a cloze-style prediction task, achieving impressive results. Many prompt-tuning-based methods focus on one or two classic scenarios with concrete external knowledge enhancement. However, when facing intricate information in multi-domain stance detection, these methods cannot be adaptive to multi-domain semantics. In this paper, we propose a novel target-adaptive consistency enhanced prompt-tuning method (TCP) for stance detection with multiple domains. TCP incorporates target knowledge and prior knowledge to construct target-adaptive verbalizers for diverse domains and employs pilot experiments distillation to enhance the consistency between verbalizers and model training. Specifically, to capture the knowledge from multiple domains, TCP uses a target-adaptive candidate mining strategy to obtain the domain-related candidates. Then, TCP refines them with prior attributes to ensure prediction consistency. The Pre-trained Language Models (PLMs) in prompt-tuning are with large-scale parameters, while only changing the verbalizer without corresponding tuning has a limited impact on the training process. Target-aware pilot experiments are conducted to enhance the consistency between the verbalizer and training by distilling the target-adaptive knowledge into prompt-tuning. Extensive experiments and ablation studies demonstrate that TCP outperforms the state-of-the-art methods on nine stance detection datasets from multiple domains.

Legal Judgement Prediction (LJP) is the task of automatically predicting a law case’s judgment results given a text describing the case’s facts, which has great prospects in judicial assistance systems and handy services for the public. In practice, confusing charges are often presented, because law cases applicable to similar law articles are easily misjudged. To address this issue, existing work relies heavily on domain experts, which hinders its application in different law systems. In this paper, we present an end-to-end model, LADAN, to solve the task of LJP. To distinguish confusing charges, we propose a novel graph neural network, GDL, to automatically learn subtle differences between confusing law articles, and also design a novel attention mechanism that fully exploits the learned differences to attentively extract effective discriminative features from fact descriptions. Experiments conducted on real-world datasets demonstrate the superiority of our LADAN.