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 …

Data augmentation has recently seen increased interest in NLP due to more work in low-
resource domains, new tasks, and the popularity of large-scale neural networks that require …

Neural Machine Translation (NMT) has seen tremendous growth in the last ten years since
the early 2000s and has already entered a mature phase. While considered the most widely …

This paper presents the results of the premier shared task organized alongside the
Conference on Machine Translation (WMT) 2019. Participants were asked to build machine …

NLP has achieved great progress in the past decade through the use of neural models and
large labeled datasets. The dependence on abundant data prevents NLP models from being …

Simple yet effective data augmentation techniques have been proposed for sentence-level
and sentence-pair natural language processing tasks. Inspired by these efforts, we design …

Despite their empirical success, neural networks still have difficulty capturing compositional
aspects of natural language. This work proposes a simple data augmentation approach to …

Low-resource Relation Extraction (LRE) aims to extract relation facts from limited labeled
corpora when human annotation is scarce. Existing works either utilize self-training scheme …

The principal task in supervised neural machine translation (NMT) is to learn to generate
target sentences conditioned on the source inputs from a set of parallel sentence pairs, and …