Multi-task Retrieval for Knowledge-Intensive Tasks

Understand

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.