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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Chapter 5, Machine Translation with the Transformer

  1. Machine translation has now exceeded human baselines. (True/False)

    False. Machine translation is one of the toughest NLP ML tasks.

  2. Machine translation requires large datasets. (True/False)


  3. There is no need to compare transformer models using the same datasets. (True/False)

    False. The only way to compare different models is to use the same datasets.

  4. BLEU is the French word for blue and is the acronym of an NLP metric. (True/False)

    True. BLEU stands for Bilingual Evaluation Understudy Score, making it easy to remember.

  5. Smoothing techniques enhance BERT. (True/False)


  6. German-English is the same as English-German for machine translation. (True/False)

    False. Representing German and then translating it into another language is not the same process as representing English and then translating it into another language. The language structures are not the same.