Before revision:  This paper improves Neu-IR in such few-shot search scenarios by meta-adaptively training neural rankers with synthetic weak supervision. We first leverage contrastive query generation (ContrastQG) to synthesize more informative queries as in-domain weak relevance labels, and then filter them with meta adaptive learning to rank (MetaLTR) to better generalize neural rankers to the target few-shot domain . Experiments on three different search domains: web, news, and biomedical , demonstrate significantly improved few-shot accuracy of neural rankers with our weak supervision framework .
After revision:  This paper improves Neu-IR in such few-shot target domains. Drawing on source-domain massive relevance supervision, MetaAdaptRank contrastively synthesizes a large number of weak supervision signals for target domains and meta-learns to reweight these synthetic "weak" data based on their benefits to the target-domain ranking accuracy of Neu-IR models . Experiments on three different search domains: web, news, and biomedical , demonstrate significantly improved few-shot accuracy of neural rankers with our weak supervision framework .