Retrieving relevant contexts from a large corpus is a crucial step for tasks such as open-domain question answering and fact checking. Although neural retrieval outperforms traditional methods like tf-idf and BM25, its performance degrades considerably when applied to out-of-domain data. Driven by the question of whether a neural retrieval model can be universal and perform robustly on a wide variety of problems, we propose a multi-task trained model. Our approach not only outperforms previous methods in the few-shot setting, but also rivals specialised neural retrievers, even when in-domain training data is abundant. With the help of our retriever, we improve existing models for downstream tasks and closely match or improve the state of the art on multiple benchmarks.
@article{arxiv.2101.00117,
title = {Multi-task Retrieval for Knowledge-Intensive Tasks},
author = {Jean Maillard and Vladimir Karpukhin and Fabio Petroni and Wen-tau Yih and Barlas Oğuz and Veselin Stoyanov and Gargi Ghosh},
journal= {arXiv preprint arXiv:2101.00117},
year = {2021}
}