ReasonChainQA: Text-based Complex Question Answering with Explainable Evidence Chains | |
Zhu MJ(朱敏郡); Weng YX(翁诣轩); He SZ(何世柱); Liu K(刘康); Zhao J(赵军) | |
2022 | |
会议日期 | 2022 |
会议地点 | 中国厦门 |
英文摘要 | 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 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. |
会议录出版者 | IEEE |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/52285] |
专题 | 模式识别国家重点实验室_自然语言处理 |
通讯作者 | Zhao J(赵军) |
作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zhu MJ,Weng YX,He SZ,et al. ReasonChainQA: Text-based Complex Question Answering with Explainable Evidence Chains[C]. 见:. 中国厦门. 2022. |
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