Keywords: question answering, information retrieval
Abstract: The task of information retrieval is an important component of many natural language processing systems, such as open domain question answering. While traditional methods were based on hand-crafted features, continuous representations based on neural networks recently obtained competitive results. A challenge of using such methods is to obtain supervised data to train the retriever model, corresponding to pairs of query and support documents. In this paper, we propose a technique to learn retriever models for downstream tasks, inspired by knowledge distillation, and which does not require annotated pairs of query and documents. Our approach leverages attention scores of a reader model, used to solve the task based on retrieved documents, to obtain synthetic labels for the retriever. We evaluate our method on question answering, obtaining state-of-the-art results.
One-sentence Summary: We show that attention scores obtained by training a model to answer questions given a set of support documents can be used to train a model to select relevant passages in a knowledge source.
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Code: [![Papers with Code](/images/pwc_icon.svg) 2 community implementations](https://paperswithcode.com/paper/?openreview=NTEz-6wysdb)
Data: [NarrativeQA](https://paperswithcode.com/dataset/narrativeqa), [Natural Questions](https://paperswithcode.com/dataset/natural-questions), [TriviaQA](https://paperswithcode.com/dataset/triviaqa)