Research on machine reading comprehension based on pre-trained model Online publication date: Mon, 31-Oct-2022
by Guanlin Chen; Rutao Yao; Haiwei Zhou; Tian Li; Wujian Yang
International Journal of Reasoning-based Intelligent Systems (IJRIS), Vol. 14, No. 4, 2022
Abstract: In order to improve the machine reading ability of the model, this article starts from the high-level semantic information of the text and the concepts of the distillation model. Among the high-level semantic information of the text, part-of-speech information and named entity information are selected as additional information that the model can obtain. Combined with part-of-speech tagging technology and named entity recognition technology, a BERT-HSI model based on a pre-training model and fusion of high-level semantic information is proposed. Later, on the basis of BERT-HSI, the paper started from the perspective of model optimisation, with the concepts in the distillation model as the core, and proposed a capability learning method. Finally, the research method about machine reading comprehension based on the pre-training model is presented, which not only integrates high-level semantic information but also includes the capacity learning process.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Reasoning-based Intelligent Systems (IJRIS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook