Knowledge Commons of Institute of Automation,CAS
Learning to Build Reasoning Chains by Reliable Path Retrieval | |
Zhu MJ(朱敏郡)![]() ![]() ![]() ![]() | |
2023 | |
会议名称 | ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
会议日期 | 2023 |
会议地点 | 希腊罗德岛 |
出版者 | IEEE |
摘要 | Question answering (QA) systems have long pursued the ability to reason over explicit knowledge credibly. Recent work has incorporated knowledge into fine-grained sentences and constructed natural language database (NLDB) task, and conducts complex QA with explicit reasoning chains. Existing models focus on retrieving evidence by combining multiple modules or discretely. However, these models ignore utilizing path information (e.g. sentence order), which is proven to be important for evidence retrievers. In this work, we propose a ReliAble Path-retrieval (RAP) to generate varying length evidence chains iteratively. It comprehensively models reasoning chains and introduces loss from two views. The experimental results show that our model demonstrates state-of-the-art performance on both evidence chain retrieval and question-answering tasks. Additional experiments on sequential supervised and sequential unsupervised retrieval fully indicate the significance of RAP. |
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/173211/52281 |
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. Learning to Build Reasoning Chains by Reliable Path Retrieval[C]:IEEE,2023. |
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 | ||
ARPR_ICASSP (5).pdf(994KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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