Book Image

Transformers for Natural Language Processing

By : Denis Rothman
Book Image

Transformers for Natural Language Processing

By: Denis Rothman

Overview of this book

The transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers. The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face. The book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. You will discover training methods for smaller transformers that can outperform GPT-3 in some cases. In the second stage, you will apply transformers for Natural Language Understanding (NLU) and Natural Language Generation (NLG). Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification. By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models by tech giants to various datasets.
Table of Contents (16 chapters)
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Getting started with SRL

SRL is as difficult for humans as for machines. However, transformers, once again, have taken a step closer to our human baselines.

In this section, we will first define SRL and visualize an example. We will then run a pretrained Bert-based model.

Let's begin by defining the problematic task of SRL.

Defining Semantic Role Labeling

Shi and Lin (2019) advanced and proved the idea that we can find who did what, and where, without depending on lexical or syntactic features. This chapter is based on Peng Shi and Jimmy Lin's research at the University of Waterloo, California. They showed how transformers learn language structures better with attention layers.

SRL labels the semantic role a word or group of words plays in a sentence and the relationship established with the predicate.

A semantic role is a role a noun or noun phrase plays in relation to the main verb in a sentence. In the sentence "Marvin walked in the park...