Papers by Santosh T.y.s.s.
CuSINeS: Curriculum-driven Structure Induced Negative Sampling for Statutory Article Retrieval (2024.lrec-main)
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| Challenge: | Existing methods to assess Statutory Article Retrieval (SAR) are vague and underspecified, resulting in a lack of clarity and a gap between legal expertise and public comprehension. |
| Approach: | They propose a negative sampling approach to enhance the performance of Statutory Article Retrieval (SAR) it employs a curriculum-based negative sampling strategy guiding the model to focus on easier negatives initially and progressively tackle more difficult ones. |
| Outcome: | The proposed approach surpasses static methods and can be used to assess the difficulty of the model. |
LexAbSumm: Aspect-based Summarization of Legal Decisions (2024.lrec-main)
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| Challenge: | LexAbSumm is a dataset designed for aspect-based summarization of legal documents . it is based on a set of ECtHR fact sheets, and is available for download. |
| Approach: | They propose a dataset designed for aspect-based summarization of legal case decisions . they evaluate abstractive summarizing models tailored for longer documents . |
| Outcome: | The proposed dataset is designed for aspect-based summarization of legal cases . it reveals a challenge in conditioning models to produce aspect-specific summaries . |
Mind Your Neighbours: Leveraging Analogous Instances for Rhetorical Role Labeling for Legal Documents (2024.lrec-main)
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| Challenge: | Rhetorical Role Labeling (RRL) of legal judgments presents challenges such as inferring sentence roles from context, interrelated roles, limited annotated data, and label imbalance. |
| Approach: | They propose techniques to enhance RRL performance by leveraging knowledge from semantically similar instances. |
| Outcome: | The proposed methods achieve remarkable improvements in challenging macro-F1 scores. |
ECtHR-PCR: A Dataset for Precedent Understanding and Prior Case Retrieval in the European Court of Human Rights (2024.lrec-main)
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| Challenge: | Prior case retrieval datasets do not simulate a realistic setting because they use complete case documents while only masking references to prior cases. |
| Approach: | They propose a prior case retrieval dataset based on judgements from the European Court of Human Rights which explicitly separate facts from arguments and exhibit precedential practices. |
| Outcome: | The proposed datasets do not simulate a realistic setting and expose queries to spurious patterns left behind by citation masks, potentially short-circuiting a comprehensive understanding of case facts and legal principles. |
Query-driven Relevant Paragraph Extraction from Legal Judgments (2024.lrec-main)
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| Challenge: | Legal professionals struggle with navigating lengthy legal judgements to pinpoint information that directly addresses their queries. |
| Approach: | They construct a specialized dataset to extract relevant paragraphs from legal judgements based on query . they assess the performance of current retrieval models in a zero-shot way . |
| Outcome: | The proposed model outperforms the current retrieval models in a zero-shot way and fine-tunes them using various models. |
Towards Explainability and Fairness in Swiss Judgement Prediction: Benchmarking on a Multilingual Dataset (2024.lrec-main)
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| Challenge: | Using Swiss Judgement Prediction, we evaluate the explainability of state-of-the-art monolingual and multilingual LJP models. |
| Approach: | They propose an occlusion-based approach to evaluate the explainability performance of legal judgement prediction models using Swiss Judgement Prediction, the only available multilingual LJP dataset. |
| Outcome: | The proposed framework allows us to quantify the influence of lower court information on model predictions, exposing current models’ biases. |