The classification of statements provided by individuals during police interviews is a complex and significant task within the domain of natural language processing (NLP) and legal informatics. The lack of extensive domain-specific datasets raises challenges to the advancement of NLP methods in the field. This paper aims to address some of the present challenges by introducing a novel dataset tailored for classification of statements made during police interviews, prior to court proceedings. Utilising the curated dataset for training and evaluation, we introduce a fine-tuned DistilBERT model that achieves state-of-the-art performance in distinguishing truthful from deceptive statements. To enhance interpretability, we employ explainable artificial intelligence (XAI) methods to offer explainability through saliency maps, that interpret the model's decision-making process. Lastly, we present an XAI interface that empowers both legal professionals and non-specialists to interact with and benefit from our system. Our model achieves an accuracy of 86%, and is shown to outperform a custom transformer architecture in a comparative study. This holistic approach advances the accessibility, transparency, and effectiveness of statement analysis, with promising implications for both legal practice and research.

该研究旨在应对自然语言处理和法律信息学领域内警务讯问中个人陈述的分类问题，通过引入一个新颖的训练集和一个经过调优的DistilBERT模型，该模型在区分真实和虚假陈述方面表现出卓越的性能，并运用可解释的人工智能方法提高解释性，最后提供一个XAI界面以提供普通人和法律专业人士互动和受益的机会。此研究通过综合方法提高陈述分析的可访问性、透明度和效果，对法律实践和研究具有重要意义。

加强审前法律分析：针对辩护陈述分类和解释的透明可访问数据集