Introduction to Transformers: an NLP Perspective
Tong Xiao Jingbo Zhu
Transformers have dominated empirical machine learning models of natural
language processing. In this paper, we introduce basic concepts of Transformers
and present key techniques that form the recent advances of these models. This
includes a description of the standard Transformer architecture, a series of
model refinements, and common applications. Given that Transformers and related
deep learning techniques might be evolving in ways we have never seen, we
cannot dive into all the model details or cover all the technical areas.
Instead, we focus on just those concepts that are helpful for gaining a good
understanding of Transformers and their variants. We also summarize the key
ideas that impact this field, thereby yielding some insights into the strengths
and limitations of these models.