Knowledge Commons of Institute of Automation,CAS
ReasonChainQA: Text-based Complex Question Answering with Explainable Evidence Chains | |
Zhu MJ(朱敏郡)![]() ![]() ![]() ![]() | |
2022 | |
会议名称 | China Automation Congress (CAC) |
会议日期 | 2022 |
会议地点 | 中国厦门 |
出版者 | IEEE |
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. |
收录类别 | EI |
语种 | 英语 |
是否为代表性论文 | 是 |
七大方向——子方向分类 | 自然语言处理 |
国重实验室规划方向分类 | 语音语言处理 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/52285 |
专题 | 多模态人工智能系统全国重点实验室_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 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 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]:IEEE,2022. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
reasonchain.pdf(456KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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