Paper Info
Title
Machine Reading Comprehension with Rich Knowledge
Abstract
Machine reading comprehension (MRC) is a crucial and challenging task in natural language processing (NLP). With the development of deep learning, language models have achieved excellent results. However, these models still cannot answer complex questions. Currently, researchers often utilize structured knowledge, such as knowledge bases (KBs), as external knowledge by directly extracting triples to enhance the results of machine reading. Although they can support certain background knowledge, the triples are limited to the interrelationships among entities or words. Unlike structured knowledge, unstructured knowledge is rich and extensive. However, these methods ignore unstructured knowledge resources, such as Wikipedia. In addition, the effect of combining the two types of knowledge is still not known. In this study, we first attempt to explore the usefulness of combining them. We introduce a fusion mechanism into a rich knowledge fusion layer (RKF) to obtain more useful and relevant knowledge from different external knowledge resources. Further to promote interaction among different types of knowledge, a bi-matching layer is added. We propose the RKF-NET framework based on BERT, and our experimental results demonstrate the effectiveness of two classic datasets: SQuAD1.1 and the Easy-Challenge (ARC).
Year
DOI
Venue
2022
10.1142/S0218001422510041
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
Keywords
DocType
Volume
Deep learning, machine reading comprehension, structured knowledge, unstructured knowledge
Journal
36
Issue
ISSN
Citations 
05
0218-0014
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Jun He17111.24
Peng Li24434.49
Yinghui Zhang363.46
Bo Sun410421.35
Rong Xiao521.75
Yongkang Xiao6608.71