The evolution of advanced sequential modeling techniques

In Chapter 10, Understanding Sequential Models, we touched upon the foundational aspects of sequential models. While they serve numerous use cases, they face challenges in grasping and producing the complex intricacies of human language.

We’ll begin our journey by discussing autoencoders. Introduced in the early 2010s, autoencoders provided a refreshing approach to data representation. They marked a significant evolution in natural language processing (NLP), transforming how we thought about data encoding and decoding. But the momentum in NLP didn’t stop there. By the mid-2010s, Seq2Seq models entered the scene, bringing forth innovative methodologies for tasks such as language translation. These models could adeptly transform one sequence form into another, heralding an era of advanced sequence processing.

However, with the rise in data complexity, the NLP community felt the need for more sophisticated...