The ability of reasoning over evidence has received increasing attention in question answering (QA). Recently, natural language database (NLDB) conducts complex QA in knowledge base with textual evidences rather than structured representations, this task attracts a lot of attention because of the flexibility and richness of textual evidence. However, existing text-based complex question answering datasets fail to provide explicit reasoning process, while it's important for retrieval effectiveness and reasoning interpretability. Therefore, we present a benchmark \textbf{ReasonChainQA} with explanatory and explicit evidence chains. ReasonChainQA consists of two subtasks: answer generation and evidence chains extraction, it also contains higher diversity for multi-hop questions with varying depths, 12 reasoning types and 78 relations. To obtain high-quality textual evidences for answering complex question. Additional experiment on supervised and unsupervised retrieval fully indicates the significance of ReasonChainQA. Dataset and codes will be made publicly available upon accepted.
翻译:对证据的推理能力在解答问题(QA)中日益受到注意。最近,自然语言数据库(NLDB)在知识库中以文字证据而不是结构化的表述形式进行复杂的质量评估,由于文本证据的灵活性和丰富性,这项任务引起许多注意;然而,现有的基于文本的复杂回答数据组问题未能提供明确的推理程序,而对于检索的有效性和解释性解释性十分重要。因此,我们提出了一个具有解释性和明确证据链的基准 \ textb{ReasonchainQA} 。理由查因QA 由两个子任务组成: 回答生成和证据链提取,它还包含不同深度、12个推理类型和78个关系的多点问题更大的多样性。要获得高质量的文本证据来回答复杂问题,关于监督和不受监督的检索的额外实验充分说明“理由”ChainQA的意义。一旦被接受,数据集和代码将公布于众。