论文标题
通过迭代文档重新播放任何跳动的开放域问题
Answering Any-hop Open-domain Questions with Iterative Document Reranking
论文作者
论文摘要
现有的开放域问答方法（QA）通常是为需要单跳或多跳推理的问题而设计的，这些问题对要回答的问题的复杂性做出了强烈的假设。同样，多步文档检索通常会引起更多相关但不支持的文档，这会抑制下游噪声敏感的读取器模块以提取答案。为了应对这些挑战，我们提出了一个统一的质量检查框架，以回答任何跳开的问题，迭代地检索，重读和过滤文档，并自适应地确定何时停止检索过程。为了提高检索准确性，我们提出了一个基于图的重新轴模型，该模型将多文件交互作用作为我们迭代的reranking框架的核心。我们的方法始终达到的性能与单跳和多跳开放域QA数据集的最先进的性能相比，包括开放的自然问题，Squad Open和HotPotQA。
Existing approaches for open-domain question answering (QA) are typically designed for questions that require either single-hop or multi-hop reasoning, which make strong assumptions of the complexity of questions to be answered. Also, multi-step document retrieval often incurs higher number of relevant but non-supporting documents, which dampens the downstream noise-sensitive reader module for answer extraction. To address these challenges, we propose a unified QA framework to answer any-hop open-domain questions, which iteratively retrieves, reranks and filters documents, and adaptively determines when to stop the retrieval process. To improve the retrieval accuracy, we propose a graph-based reranking model that perform multi-document interaction as the core of our iterative reranking framework. Our method consistently achieves performance comparable to or better than the state-of-the-art on both single-hop and multi-hop open-domain QA datasets, including Natural Questions Open, SQuAD Open, and HotpotQA.
