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 …

Dropout is a powerful and widely used technique to regularize the training of deep neural
networks. Though effective and performing well, the randomness introduced by dropout …

Amid the rapid advancement of neural machine translation, the challenge of data sparsity
has been a major obstacle. To address this issue, this study proposes a general data …

Adversarial training has been shown effective at endowing the learned representations with
stronger generalization ability. However, it typically requires expensive computation to …

We present a simple and effective pretraining strategy--bidirectional training (BiT) for neural
machine translation. Specifically, we bidirectionally update the model parameters at the …

Knowledge distillation (KD) is commonly used to construct synthetic data for training non-
autoregressive translation (NAT) models. However, there exists a discrepancy on low …

Neural approaches have achieved state-of-the-art accuracy on machine translation but
suffer from the high cost of collecting large scale parallel data. Thus, a lot of research has …

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 …

Data-hungry deep neural networks have established themselves as the de facto standard for
many NLP tasks, including the traditional sequence tagging ones. Despite their state-of-the …

Short texts (STs) present in a variety of scenarios, including query, dialog, and entity names.
Most of the exciting studies in neural machine translation (NMT) are focused on tackling …