Court View Generation (CVG) is a challenging task in the field of Legal Artificial Intelligence (LegalAI), which aims to generate court views based on the plaintiff claims and the fact descriptions. While Pretrained Language Models (PLMs) have showcased their prowess in natural language generation, their application to the complex, knowledge-intensive domain of CVG often reveals inherent limitations. In this paper, we present a novel approach, named Knowledge Injection and Guidance (KIG), designed to bolster CVG using PLMs. To efficiently incorporate domain knowledge during the training stage, we introduce a knowledge-injected prompt encoder for prompt tuning, thereby reducing computational overhead. Moreover, to further enhance the model's ability to utilize domain knowledge, we employ a generating navigator, which dynamically guides the text generation process in the inference stage without altering the model's architecture, making it readily transferable. Comprehensive experiments on real-world data demonstrate the effectiveness of our approach compared to several established baselines, especially in the responsivity of claims, where it outperforms the best baseline by 11.87%.

翻译：法庭观点生成（CVG）是法律人工智能（LegalAI）领域中的一项具有挑战性的任务，旨在根据原告主张和事实描述生成法庭意见。尽管预训练语言模型（PLMs）在自然语言生成中展示了其卓越性能，但在复杂且知识密集型的CVG任务中应用时，它们往往暴露出固有的局限性。本文提出了一种名为知识注入与引导（KIG）的新方法，旨在利用PLMs增强CVG效果。为在训练阶段高效整合领域知识，我们引入了一种知识注入的提示编码器用于提示调优，从而降低计算开销。此外，为进一步提升模型利用领域知识的能力，我们采用了一种生成导航器，在推理阶段无需改动模型架构即可动态引导文本生成过程，使其易于迁移。基于真实数据的综合实验表明，我们的方法相较于多个已有基准模型具有显著优势，尤其在主张响应性方面，其性能比最佳基准模型高出11.87%。