Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer

• URL: http://arxiv.org/abs/2212.02027v1
• Date: Mon, 5 Dec 2022 04:51:21 GMT
• Title: Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer
• Authors: Zhengbao Jiang, Luyu Gao, Jun Araki, Haibo Ding, Zhiruo Wang, Jamie Callan, Graham Neubig
• Abstract summary: We show that a single model trained end-to-end can achieve both competitive retrieval and QA performance. We show that end-to-end adaptation significantly boosts its performance on out-of-domain datasets in both supervised and unsupervised settings.
• Score: 80.50327229467993
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Systems for knowledge-intensive tasks such as open-domain question answering (QA) usually consist of two stages: efficient retrieval of relevant documents from a large corpus and detailed reading of the selected documents to generate answers. Retrievers and readers are usually modeled separately, which necessitates a cumbersome implementation and is hard to train and adapt in an end-to-end fashion. In this paper, we revisit this design and eschew the separate architecture and training in favor of a single Transformer that performs Retrieval as Attention (ReAtt), and end-to-end training solely based on supervision from the end QA task. We demonstrate for the first time that a single model trained end-to-end can achieve both competitive retrieval and QA performance, matching or slightly outperforming state-of-the-art separately trained retrievers and readers. Moreover, end-to-end adaptation significantly boosts its performance on out-of-domain datasets in both supervised and unsupervised settings, making our model a simple and adaptable solution for knowledge-intensive tasks. Code and models are available at https://github.com/jzbjyb/ReAtt.

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