Advertisement
Published in:
2021 | OriginalPaper | Chapter
A Comparative Study of Text Classification and Missing Word Prediction Using BERT and ULMFiT
Authors : Praveenkumar Katwe, Aditya Khamparia, Kali Prasad Vittala, Ojas Srivastava
Published in: Evolutionary Computing and Mobile Sustainable Networks
Publisher: Springer Singapore
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Abstract
We perform a comparative study on the two types of emerging NLP models, ULMFiT and BERT. To gain insights on the suitability of these models to industry-relevant tasks, we use Text classification and Missing word prediction and emphasize how these two tasks can cover most of the prime industry use cases. We systematically frame the performance of the above two models by using selective metrics and train them with various configurations and inputs. This paper is intended to assist the industry researchers on the pros and cons of fine-tuning the industry data with these two pre-trained language models for obtaining the best possible state-of-the-art results.