Author : Aman Pathak
Affiliation : Medi-Caps University
Country : India
Category : Computer Science & Information Technology
Volume, Issue, Month, Year : 11, 01, January, 2021
Natural language processing (NLP) has witnessed many substantial advancements in the past three years. With the introduction of the Transformer and self-attention mechanism, language models are now able to learn better representations of the natural language. These attentionbased models have achieved exceptional state-of-the-art results on various NLP benchmarks. One of the contributing factors is the growing use of transfer learning. Models are pre-trained on unsupervised objectives using rich datasets that develop fundamental natural language abilities that are fine-tuned further on supervised data for downstream tasks. Surprisingly, current researches have led to a novel era of powerful models that no longer require finetuning. The objective of this paper is to present a comparative analysis of some of the most influential language models. The benchmarks of the study are problem-solving methodologies, model architecture, compute power, standard NLP benchmark accuracies and shortcomings.
Keyword : Natural Language Processing, Transformers, Attention-Based Models, Representation Learning, Transfer Learning
For More Details : https://aircconline.com/csit/papers/vol11/csit110111.pdf