Text Reasoning Chain Extraction for Multi-Hop Question Answering  

作　　者：Pengming Wang Zijiang Zhu Qing Chen Weihuang Dai 

机构地区：[1]School of Data Science and Artificial Intelligence,Wenzhou University of Technology,Wenzhou 325035,China [2]School of Computer Science,and also with Institute of Intelligent Information Processing,South China Business College,Guangdong University of Foreign Studies,Guangzhou 510545,China [3]School of Psychology,Jiangxi Normal University,Nanchang 330022,China [4]also with School of Science,East China Jiaotong University,Nanchang 330013,China [5]Institute of Intelligent Information Processing,South China Business College,Guangdong University of Foreign Studies,Guangzhou 510545,China

出　　处：《Tsinghua Science and Technology》2024年第4期959-970,共12页清华大学学报自然科学版（英文版）

摘　　要：With the advent of the information age, it will be more troublesome to search for a lot of relevant knowledge to find the information you need. Text reasoning is a very basic and important part of multi-hop question and answer tasks. This paper aims to study the integrity, uniformity, and speed of computational intelligence inference data capabilities. That is why multi-hop reasoning came into being, but it is still in its infancy, that is, it is far from enough to conduct multi-hop question and answer questions, such as search breadth, process complexity, response speed, comprehensiveness of information, etc. This paper makes a text comparison between traditional information retrieval and computational intelligence through corpus relevancy and other computing methods. The study finds that in the face of multi-hop question and answer reasoning, the reasoning data that traditional retrieval methods lagged behind in intelligence are about 35% worse. It shows that computational intelligence would be more complete, unified, and faster than traditional retrieval methods. This paper also introduces the relevant points of text reasoning and describes the process of the multi-hop question answering system, as well as the subsequent discussions and expectations.

关 键 词：intelligent computing multi-hop quiz text reasoning document retrieval text complex network 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] R445[自动化与计算机技术—计算机科学与技术]