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DUQGen: Effective Unsupervised Domain Adaptation of Neural Rankers by Diversifying Synthetic Query Generation
April 4, 2024, 4:47 a.m. | Ramraj Chandradevan, Kaustubh D. Dhole, Eugene Agichtein
cs.CL updates on arXiv.org arxiv.org
Abstract: State-of-the-art neural rankers pre-trained on large task-specific training data such as MS-MARCO, have been shown to exhibit strong performance on various ranking tasks without domain adaptation, also called zero-shot. However, zero-shot neural ranking may be sub-optimal, as it does not take advantage of the target domain information. Unfortunately, acquiring sufficiently large and high quality target training data to improve a modern neural ranker can be costly and time-consuming. To address this problem, we propose a …
abstract art arxiv cs.cl cs.ir data domain domain adaptation however performance query ranking state synthetic tasks task-specific training training training data type unsupervised zero-shot
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