As an effective strategy, data augmentation (DA) alleviates data scarcity scenarios where
deep learning techniques may fail. It is widely applied in computer vision then introduced to …

Data augmentation, the artificial creation of training data for machine learning by
transformations, is a widely studied research field across machine learning disciplines …

Most previous methods for text data augmentation are limited to simple tasks and weak
baselines. We explore data augmentation on hard tasks (ie, few-shot natural language …

Multi-label text classification (MLTC) involves tagging a document with its most relevant
subset of labels from a label set. In real applications, labels usually follow a long-tailed …

In the rapidly evolving field of machine learning (ML), data augmentation (DA) has emerged
as a pivotal technique for enhancing model performance by diversifying training examples …

Data augmentation is an effective approach to tackle over-fitting. Many previous works have
proposed different data augmentations strategies for NLP, such as noise injection, word …

We study a family of data augmentation methods, substructure substitution (SUB2), for
natural language processing (NLP) tasks. SUB2 generates new examples by substituting …

We introduce GENIUS: a conditional text generation model using sketches as input, which
can fill in the missing contexts for a given sketch (key information consisting of textual spans …

One of the main challenges in conversational question answering (CQA) is to resolve the
conversational dependency, such as anaphora and ellipsis. However, existing approaches …

Generating high quality question-answer pairs is a hard but meaningful task. Although
previous works have achieved great results on answer-aware question generation, it is …