What are the best methods for data augmentation when training Keras models for text input

Question

With the help of code can you tell me What are the best methods for data augmentation when training Keras models for text input?

Answer 1

The best data augmentation methods for text input in Keras include synonym replacement (WordNet), back-translation, random word insertion/deletion, paraphrasing with LLMs, and contextual embeddings (Word2Vec, BERT) to generate diverse training samples.

Here is the code snippet given below:

In the above code we are using the following techniques:

• Synonym Replacement (WordNet/NLPAug):

  • Replaces words with synonyms while preserving sentence meaning.

• Back-Translation (Helsinki-NLP):

  • Translates text to another language and back for natural variation.

• Random Word Insertion & Deletion:

  • Adds noise and diversity to prevent overfitting.

• Contextual Embedding Augmentation (BERT/Word2Vec):

  • Replaces words with semantically similar embeddings for realistic variations.

• Paraphrasing with LLMs (GPT-3, T5, Pegasus):

  • Generates syntactically diverse yet semantically equivalent sentences.

Hence, using synonym replacement, back-translation, word manipulations, and embedding-based transformations significantly enhances text dataset diversity for robust Keras models.