Before revision:  Neural Information Retrieval (Neu-IR) models have shown their effectiveness and thrive from end-to-end training with massive high-quality relevance labels. Nevertheless, relevance labels at such quantity are luxury and unavailable in many ranking scenarios, for example, in biomedical search. This paper improves Neu-IR in such few-shot search scenarios by meta-adaptively training neural rankers with synthetic weak supervision.
After revision:  Neural Information Retrieval (Neu-IR) often depends on a large scale of in-domain relevance training signals, which are not always available in real-world ranking scenarios. To democratize the benefits of Neu-IR in such few-shot search scenarios by meta-adaptively training neural rankers with synthetic weak supervision.