nlpaug 0.0.8

Natural language processing augmentation library for deep neural networks

nlpaug

This python library helps you with augmenting nlp for your machine learning projects. Visit this introduction to understand about Data Augmentation in NLP. Augmenter is the basic element of augmentation while Flow is a pipeline to orchestra multi augmenter together.

• Data Augmentation library for Text
• Data Augmentation library for Speech Recognition
• Data Augmentation library for Audio
• Does your NLP model able to prevent adversarial attack?

Starter Guides

• Augmentation for character and word
• Augmentation for spectrogram (audio input)
• Augmentation for audio
• How to train TF-IDF model
• How to create custom augmentation
• API Documentation

Flow

Pipeline	Description
Sequential	Apply list of augmentation functions sequentially
Sometimes	Apply some augmentation functions randomly

Textual Augmenter

Target	Augmenter	Action	Description
Character	RandomAug	insert	Insert character randomly
		substitute	Substitute character randomly
		swap	Swap character randomly
		delete	Delete character randomly
	OcrAug	substitute	Simulate OCR engine error
	KeyboardAug	substitute	Simulate keyboard distance error
Word	RandomWordAug	swap	Swap word randomly
		delete	Delete word randomly
	SpellingAug	substitute	Substitute word according to spelling mistake dictionary
	WordNetAug	substitute	Substitute word according to WordNet's synonym
	WordEmbsAug	insert	Insert word randomly from word2vec, GloVe or fasttext dictionary
		substitute	Substitute word based on word2vec, GloVe or fasttext embeddings
	TfIdfAug	insert	Insert word randomly trained TF-IDF model
		substitute	Substitute word based on TF-IDF score
	ContextualWordEmbsAug	insert	Insert word based by feeding surroundings word to BERT and XLNet language model
		substitute	Substitute word based by feeding surroundings word to BERT and XLNet language model
Sentence	ContextualWordEmbsForSentenceAug	insert	Insert sentence according to GPT2 or XLNet prediction

Signal Augmenter

Target	Augmenter	Action	Description
Audio	NoiseAug	substitute	Inject noise
	PitchAug	substitute	Adjust audio's pitch
	ShiftAug	substitute	Shift time dimension forward/ backward
	SpeedAug	substitute	Adjust audio's speed
	CropAug	delete	Delete audio's segment
	LoudnessAug	substitute	Adjust audio's volume
	MaskAug	substitute	Mask audio's segment
Spectrogram	FrequencyMaskingAug	substitute	Set block of values to zero according to frequency dimension
	TimeMaskingAug	substitute	Set block of values to zero according to time dimension

Installation

The library supports python 3.5+ in linux and window platform.

To install the library:

pip install nlpaug numpy matplotlib python-dotenv

or install the latest version (include BETA features) from github directly

pip install git+https://github.com/makcedward/nlpaug.git

If you use ContextualWordEmbsAug, install the following dependencies as well

pip install torch>=1.1.0 pytorch_pretrained_bert>=1.1.0

If you use WordNetAug, install the following dependencies as well

pip install nltk

If you use WordEmbsAug (word2vec, glove or fasttext), downloading pre-trained model first

from nlpaug.util.file.download import DownloadUtil
DownloadUtil.download_word2vec(dest_dir='.') # Download word2vec model
DownloadUtil.download_glove(model_name='glove.6B', dest_dir='.') # Download GloVe model
DownloadUtil.download_fasttext(model_name='wiki-news-300d-1M', dest_dir='.') # Download fasttext model

If you use any one of audio augmenter, install the following dependencies as well

pip install librosa

Recent Changes

0.0.8 Sep 4, 2019

• BertAug is replaced by ContextualWordEmbsAug
• Support GPU (for ContextualWordEmbsAug only) #26
• Upgraded pytorch_transformer to 1.1.0 version #33
• ContextualWordEmbsAug suuports both BERT and XLNet model
• Removed librosa dependency
• Add ContextualWordEmbsForSentenceAug for generating next sentence
• Fix sampling issue #38

See changelog for more details.

Source

The library contains the usage of the following pre-trained model:

• word2vec (Google): Tomas Mikolov, Kai Chen, Greg Corrado and Jeffrey Dean released Efficient Estimation of Word Representations in Vector Space
• GloVe (Standford): Jeffrey Pennington, Richard Socher, and Christopher D. Manning released GloVe: Global Vectors for Word Representation
• fastText (Facebook): Tomas Mikolov, Edouard Grave, Piotr Bojanowski, Christian Puhrsch and Armand Joulin released Advances in Pre-Training Distributed Word Representations
• BERT (Google): Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova released BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Used Hugging Face PyTorch version.
• XLNet (Google/CMU): Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le released XLNet: Generalized Autoregressive Pretraining for Language Understanding. Used Hugging Face PyTorch version.

The library also captured data from internet for building augmenter/ test case. See data source for more details.

Research Reference

Some of the above augmenters are inspired by the following research papers. However, it does not always follow original implementation due to different reasons. If original implementation is needed, please refer to original source code.

• Y. Belinkov and Y. Bisk. Synthetic and Natural Noise Both Break Neural Machine Translation. 2017
• J. Ebrahimi, A. Rao, D. Lowd and D. Dou. HotFlip: White-Box Adversarial Examples for Text Classification. 2018
• J. Ebrahimi, D. Lowd and Dou. On Adversarial Examples for Character-Level Neural Machine Translation. 2018
• D. Pruthi, B. Dhingra and Z. C. Lipton. Combating Adversarial Misspellings with Robust Word Recognition. 2019
• T. Niu and M. Bansal. Adversarial Over-Sensitivity and Over-Stability Strategies for Dialogue Models. 2018
• P. Minervini and S. Riedel. Adversarially Regularising Neural NLI Models to Integrate Logical Background Knowledge. 2018
• X. Zhang, J. Zhao and Y. LeCun. Character-level Convolutional Networks for Text Classification. 2015
• S. Kobayashi and C. Coulombe. Text Data Augmentation Made Simple By Leveraging NLP Cloud APIs. 2018
• Q. Xie, Z. Dai, E Hovy, M. T. Luong and Q. V. Le. Unsupervised Data Augmentation. 2019
• W. Y. Wang and D. Yang. That’s So Annoying!!!: A Lexical and Frame-Semantic Embedding Based Data Augmentation Approach to Automatic Categorization of Annoying Behaviors using #petpeeve Tweets. 2015
• S. Kobayashi. Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relation. 2018
• D. S. Park, W. Chan, Y. Zhang, C. C. Chiu, B. Zoph, E. D. Cubuk and Q. V. Le. SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition. 2019
• R. Jia and P. Liang. Adversarial Examples for Evaluating Reading Comprehension Systems. 2017