CuSINeS: Curriculum-driven Structure Induced Negative Sampling for Statutory Article Retrieval

Santosh T.y.s.s., Kristina Kaiser, Matthias Grabmair

Abstract
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.
Anthology ID:
2024.lrec-main.381
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
4266–4272
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/2024.lrec-main.381/
DOI:
Bibkey:
Cite (ACL):
Santosh T.y.s.s., Kristina Kaiser, and Matthias Grabmair. 2024. CuSINeS: Curriculum-driven Structure Induced Negative Sampling for Statutory Article Retrieval. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 4266–4272, Torino, Italia. ELRA and ICCL.
Cite (Informal):
CuSINeS: Curriculum-driven Structure Induced Negative Sampling for Statutory Article Retrieval (T.y.s.s. et al., LREC-COLING 2024)
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https://preview.aclanthology.org/build-pipeline-with-new-library/2024.lrec-main.381.pdf
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