Book Image

Natural Language Processing with TensorFlow

By : Motaz Saad, Thushan Ganegedara
Book Image

Natural Language Processing with TensorFlow

By: Motaz Saad, Thushan Ganegedara

Overview of this book

Natural language processing (NLP) supplies the majority of data available to deep learning applications, while TensorFlow is the most important deep learning framework currently available. Natural Language Processing with TensorFlow brings TensorFlow and NLP together to give you invaluable tools to work with the immense volume of unstructured data in today’s data streams, and apply these tools to specific NLP tasks. Thushan Ganegedara starts by giving you a grounding in NLP and TensorFlow basics. You'll then learn how to use Word2vec, including advanced extensions, to create word embeddings that turn sequences of words into vectors accessible to deep learning algorithms. Chapters on classical deep learning algorithms, like convolutional neural networks (CNN) and recurrent neural networks (RNN), demonstrate important NLP tasks as sentence classification and language generation. You will learn how to apply high-performance RNN models, like long short-term memory (LSTM) cells, to NLP tasks. You will also explore neural machine translation and implement a neural machine translator. After reading this book, you will gain an understanding of NLP and you'll have the skills to apply TensorFlow in deep learning NLP applications, and how to perform specific NLP tasks.
Table of Contents (16 chapters)
Natural Language Processing with TensorFlow
Contributors
Preface
Index

Training an NMT jointly with word embeddings


Here we will discuss how we can train an NMT jointly with word embeddings. We will be covering two concepts in this section:

  • Training an NMT jointly with a word embedding layer

  • Using pretrained embeddings instead of randomly initializing the embeddings layer

There are several multilingual word embedding repositories available:

From these, we will use the CMU embeddings (~200 MB) as it's much smaller compared with fastText (~5 GB). We first need to download the German (multilingual_embeddings.de) and English (multilingual_embeddings.en) embeddings. This is available as an exercise in nmt_with_pretrained_wordvecs.ipynb in the ch10 folder.

Maximizing matchings between the dataset vocabulary and the pretrained embeddings

We will first have to get a subset of the...