A Machine Reading Comprehension Approach Based on Reading Skill Recognition and Dual Channel Fusion Mechanism
Machine reading comprehension task aims to require the system to understand a given passage and then answer a question.Previous researches focus on the interaction between questions and passages.However,they neg-lect to make a more granular analysis of the questions,e.g.,what is the reading skill examined by the questions?In-spired by the previous reading comprehension literature,the understanding of questions is a multi-dimensional pro-cess where humans first need to understand the context semantics of the question,then identify the reading skills they need to use for different types of questions,and finally answer the question.In the end,we propose a machine reading comprehension method based on reading skill recognition and dual channel fusion mechanism to make a comprehensive analysis of questions,so as to improve the accuracy of the model in answering questions.Specifically,the reading skill recognizer can capture the semantic representations of reading skills through contrastive learning.The dual channel fusion mechanism deeply integrates the contextual information and the semantic representations of reading skills,so as to help the system understand the question and passage.To verify the effectivenesss of the model,we conduct experiments on the FairytaleQA dataset.The experimental results show that the proposed meth-od achieves the state-of-the-art performance on machine reading comprehension task and reading skill recognition task.
万方数据
维普
