CuSINeS: Curriculum-driven Structure Induced Negative Sampling for Statutory Article Retrieval
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866929298480300032 |
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| author | Santosh, T. Y. S. S Kaiser, Kristina Grabmair, Matthias |
| author_facet | Santosh, T. Y. S. S Kaiser, Kristina Grabmair, Matthias |
| contents | In this paper, we introduce CuSINeS, a negative sampling approach to enhance the performance of Statutory Article Retrieval (SAR). CuSINeS offers three key contributions. Firstly, it employs a curriculum-based negative sampling strategy guiding the model to focus on easier negatives initially and progressively tackle more difficult ones. Secondly, it leverages the hierarchical and sequential information derived from the structural organization of statutes to evaluate the difficulty of samples. Lastly, it introduces a dynamic semantic difficulty assessment using the being-trained model itself, surpassing conventional static methods like BM25, adapting the negatives to the model's evolving competence. Experimental results on a real-world expert-annotated SAR dataset validate the effectiveness of CuSINeS across four different baselines, demonstrating its versatility. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_00590 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CuSINeS: Curriculum-driven Structure Induced Negative Sampling for Statutory Article Retrieval Santosh, T. Y. S. S Kaiser, Kristina Grabmair, Matthias Information Retrieval Computation and Language In this paper, we introduce CuSINeS, a negative sampling approach to enhance the performance of Statutory Article Retrieval (SAR). CuSINeS offers three key contributions. Firstly, it employs a curriculum-based negative sampling strategy guiding the model to focus on easier negatives initially and progressively tackle more difficult ones. Secondly, it leverages the hierarchical and sequential information derived from the structural organization of statutes to evaluate the difficulty of samples. Lastly, it introduces a dynamic semantic difficulty assessment using the being-trained model itself, surpassing conventional static methods like BM25, adapting the negatives to the model's evolving competence. Experimental results on a real-world expert-annotated SAR dataset validate the effectiveness of CuSINeS across four different baselines, demonstrating its versatility. |
| title | CuSINeS: Curriculum-driven Structure Induced Negative Sampling for Statutory Article Retrieval |
| topic | Information Retrieval Computation and Language |
| url | https://arxiv.org/abs/2404.00590 |