Current open-domain question answering (QA) systems often follow a Retriever-Reader (R2) architecture, where the retriever first retrieves relevant passages and the reader then reads the retrieved passages to form an answer. In this paper, we propose a simple and effective passage reranking method, Reader-guIDEd Reranker (Rider), which does not involve any training and reranks the retrieved passages solely based on the top predictions of the reader before reranking. We show that Rider, despite its simplicity, achieves 10 to 20 absolute gains in top-1 retrieval accuracy and 1 to 4 Exact Match (EM) score gains without refining the retriever or reader. In particular, Rider achieves 48.3 EM on the Natural Questions dataset and 66.4 on the TriviaQA dataset when only 1,024 tokens (7.8 passages on average) are used as the reader input.

中文翻译：

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读者指导的开放域问题答卷评分

当前的开放域问答系统（QA）通常遵循Retriever-Reader（R2）体系结构，在该体系结构中，检索器首先检索相关段落，然后阅读器读取所检索的段落以形成答案。在本文中，我们提出了一种简单有效的文章重新排名方法，即“读者指导的重新排名”（Rider），该方法无需进行任何培训，仅根据读者在重新排名之前的最高预测对获得的文章进行重新排名。我们显示，尽管Rider简单易用，但在不改进检索器或阅读器的情况下，可以在前1名的检索准确性中获得10至20个绝对增益，在精确匹配（EM）方面获得1至4个分数。特别是，当仅将1,024个标记（平均7.8个段落）用作阅读器输入时，Rider在Natural Questions数据集上达到48.3 EM，在TriviaQA数据集上达到66.4 EM。