Neural NLP systems achieve high scores in the presence of sizable training dataset. Lack of such datasets leads to poor system performances in the case low-resource languages. We present two simple text augmentation techniques using dependency trees, inspired from image processing. We crop sentences by removing dependency links, and we rotate sentences by moving the tree fragments around the root. We apply these techniques to augment the training sets of low-resource languages in Universal Dependencies project. We implement a character-level sequence tagging model and evaluate the augmented datasets on part-of-speech tagging task. We show that crop and rotate provides improvements over the models trained with non-augmented data for majority of the languages, especially for languages with rich case marking systems.
@cite_3: This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.
Similar to sentence cropping, define transformation rules to simplify sentences (e.g., I was not given a chance to eat - I ate) and shows that enrichening training set with simplified sentences improves the results of semantic role labeling. One of the first studies in text augmentation @cite_3 , replaces a randomly chosen word with its randomly chosen synonym extracted from a thesaurus. They report improved test scores when a large neural model is trained with the augmented set. induce grammar from semantic parsing training data and generate new data points by sampling to feed a sequence to sequence RNN model. chooses low-frequency words instead of a random word, and generate synthetic sentence pairs that contain those rare words.