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Natural Language Processing with TensorFlow

You're reading from   Natural Language Processing with TensorFlow The definitive NLP book to implement the most sought-after machine learning models and tasks

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Product type Paperback
Published in Jul 2022
Publisher Packt
ISBN-13 9781838641351
Length 514 pages
Edition 2nd Edition
Languages
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Author (1):
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Thushan Ganegedara Thushan Ganegedara
Author Profile Icon Thushan Ganegedara
Thushan Ganegedara
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Table of Contents (15) Chapters Close

Preface 1. Introduction to Natural Language Processing FREE CHAPTER 2. Understanding TensorFlow 2 3. Word2vec – Learning Word Embeddings 4. Advanced Word Vector Algorithms 5. Sentence Classification with Convolutional Neural Networks 6. Recurrent Neural Networks 7. Understanding Long Short-Term Memory Networks 8. Applications of LSTM – Generating Text 9. Sequence-to-Sequence Learning – Neural Machine Translation 10. Transformers 11. Image Captioning with Transformers 12. Other Books You May Enjoy
13. Index
Appendix A: Mathematical Foundations and Advanced TensorFlow

The BLEU score – evaluating the machine translation systems

BLEU stands for Bilingual Evaluation Understudy and is a way of automatically evaluating machine translation systems. This metric was first introduced in the paper BLEU: A Method for Automatic Evaluation of Machine Translation, Papineni and others, Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia, July 2002: 311-318. We will be using an implementation of the BLEU score found at https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py. Let’s understand how this is calculated in the context of machine translation.

Let’s consider an example to learn the calculations of the BLEU score. Say we have two candidate sentences (that is, a sentence predicted by our MT system) and a reference sentence (that is, the corresponding actual translation) for some given source sentence:

  • Reference 1: The cat sat on the mat
  • Candidate...
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