Empowering Prior to Court Legal Analysis: A Transparent and Accessible Dataset for Defensive Statement Classification and Interpretation
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910450691604480 |
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| author | Spyridis, Yannis Jean-Paul Deeb, Haneen Argyriou, Vasileios |
| author_facet | Spyridis, Yannis Jean-Paul Deeb, Haneen Argyriou, Vasileios |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_10702 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Empowering Prior to Court Legal Analysis: A Transparent and Accessible Dataset for Defensive Statement Classification and Interpretation Spyridis, Yannis Jean-Paul Deeb, Haneen Argyriou, Vasileios Computation and Language Computer Vision and Pattern Recognition 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. |
| title | Empowering Prior to Court Legal Analysis: A Transparent and Accessible Dataset for Defensive Statement Classification and Interpretation |
| topic | Computation and Language Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.10702 |