Abstract
A boom in applying deep learning (DL) models over the past several years has advanced the discipline of NLP. Firstly, the theoretical foundations of artificial intelligence and NLP are briefly introduced in this survey. Then, it sorts through much recent research and compiles many pertinent contributions. Lately, this article has introduced optimization theory and techniques for neural network training. First, we classify and discuss the various facets and NLP applications profiting from deep learning. Second, we review generic language modelling methods used in pre-training neural networks, such as BERT, RoBERT, AlBERT and DeBERT. Third, we compared the different language models in GLUE, MNL1, and SQuAD for accuracy and efficiency for best optimization.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
