This paper is concerned with open-domain question answering (i.e., OpenQA). Recently, some works have viewed this problem as a reading comprehension (RC) task, and directly applied successful RC models to it. However, the performances of such models are not so good as that in the RC task. In our opinion, the perspective of RC ignores three characteristics in OpenQA task: 1) many paragraphs without the answer span are included in the data collection; 2) multiple answer spans may exist within one given paragraph; 3) the end position of an answer span is dependent with the start position. In this paper, we first propose a new probabilistic formulation of OpenQA, based on a three-level hierarchical structure, i.e.,~the question level, the paragraph level and the answer span level. Then a Hierarchical Answer Spans Model (HAS-QA) is designed to capture each probability. HAS-QA has the ability to tackle the above three problems, and experiments on public OpenQA datasets show that it significantly outperforms traditional RC baselines and recent OpenQA baselines.

本文提出了一种新的概率形式化OpenQA方法，基于三层分层结构，即问题级别、段落级别和答案段水平，以及设计了适应于此的分层答案段模型（HAS-QA），能够解决传统RC、OpenOA和多个答案范围的起始/结束位置相关的问题，实验表明，其性能显著优于现有的OpenQA基线方法。

HAS-QA: 用于开放领域问答的层次化答案跨度模型