We present an end-to-end differentiable training method for retrieval-augmented open-domain question answering systems that combine information from multiple retrieved documents when generating answers. We model retrieval decisions as latent variables over sets of relevant documents. Since marginalizing over sets of retrieved documents is computationally hard, we approximate this using an expectation-maximization algorithm. We iteratively estimate the value of our latent variable (the set of relevant documents for a given question) and then use this estimate to update the retriever and reader parameters. We hypothesize that such end-to-end training allows training signals to flow to the reader and then to the retriever better than staged-wise training. This results in a retriever that is able to select more relevant documents for a question and a reader that is trained on more accurate documents to generate an answer. Experiments on three benchmark datasets demonstrate that our proposed method outperforms all existing approaches of comparable size by 2-3% absolute exact match points, achieving new state-of-the-art results. Our results also demonstrate the feasibility of learning to retrieve to improve answer generation without explicit supervision of retrieval decisions.

中文翻译：

---

用于开放域问答的多文档阅读器和检索器的端到端培训

我们提出了一种用于检索增强型开放域问答系统的端到端可区分训练方法，该系统在生成答案时结合了来自多个检索到的文档的信息。我们将检索决策建模为相关文档集上的潜在变量。由于对检索到的文档集进行边缘化在计算上很困难，因此我们使用期望最大化算法对此进行近似。我们迭代地估计我们的潜在变量（给定问题的相关文档集）的值，然后使用这个估计来更新检索器和阅读器参数。我们假设这种端到端的训练比分阶段训练更能让训练信号流向阅读器，然后流向检索器。这导致检索器能够为问题选择更多相关文档，而阅读器则通过更准确的文档进行训练以生成答案。在三个基准数据集上的实验表明，我们提出的方法在绝对精确匹配点方面比所有现有方法的可比大小高出 2-3%，实现了新的最先进的结果。我们的结果还证明了在没有明确监督检索决策的情况下学习检索以改进答案生成的可行性。