Data Augmentation in NLP

Explore data augmentation in NLP by using word embeddings to replace keywords with similar words, expanding datasets while maintaining relevance through supervised filtering.

Apply data augmentation in natural language processing by using word embeddings in Google Colab, with Gensim and NumPy to compute cosine similarity and find similar words for dataset expansion.

Use BERT for data augmentation by masking words and predicting masked words with contextual understanding to expand datasets, highlighting advantages over word embeddings and multilingual capabilities.

Explore data augmentation in NLP using BERT, building a masked word prediction pipeline with the transformers library and bert-base-uncased, including multilingual BERT for multiple languages.

Explore data augmentation in NLP using back translation to generate paraphrases and varied sentences. Configure top results and multiple models to expand outputs, then filter duplicates to improve data quality.

Implement back translation data augmentation using fairseq models to translate English to German and back to English. Expand the dataset by generating multiple outputs with beam sizes.

Explore data augmentation with text-to-text transfer transformer T5, including summarization, translation, and paraphrase generation, plus fine-tuning approaches using PAWS for diverse NLP tasks.

Fine-tune a T5 model for paraphrase generation using the simple transformers library on the PAWS dataset. Learn to save, load, and generate domain-specific paraphrases with top-k and top-p sampling.

Apply a similarity filter to improve augmented data quality in NLP by removing duplicates and irrelevant variations with lemmatization, stop word removal, and semantic similarity via the universal sentence encoder.

data augmentation ensemble approach explains how to combine four techniques to generate NLP data, using BERT masking, word embeddings, back translation, and T5 paraphrasing, with similarity filtering to remove duplicates.

Compare data augmentation techniques including word embeddings, BERT, back translation, T5 paraphrase generation, and an ensemble approach with similarity filter. Assess computation power, quality, and human supervision across methods.