April 2, 2024, 7:52 p.m. | T. Y. S. S Santosh, Kristina Kaiser, Matthias Grabmair

cs.CL updates on arXiv.org arxiv.org

arXiv:2404.00590v1 Announce Type: cross
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 …

abstract article arxiv cs.cl cs.ir curriculum focus key negative paper performance retrieval sampling strategy type

Founding AI Engineer, Agents

@ Occam AI | New York

AI Engineer Intern, Agents

@ Occam AI | US

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Business Intelligence Architect - Specialist

@ Eastman | Hyderabad, IN, 500 008