Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …

Abstract Grammatical Error Correction (GEC) is the task of automatically detecting and
correcting errors in text. The task not only includes the correction of grammatical errors, such …

Large language models have been shown to achieve remarkable performance across a
variety of natural language tasks using few-shot learning, which drastically reduces the …

This paper explores a simple method for improving the zero-shot learning abilities of
language models. We show that instruction tuning--finetuning language models on a …

The main challenge in the field of unsupervised machine translation (UMT) is to associate
source-target sentences in the latent space. As people who speak different languages share …

We demonstrate that scaling up language models greatly improves task-agnostic, few-shot
performance, sometimes even becoming competitive with prior state-of-the-art fine-tuning …

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 …

The recently proposed BERT has shown great power on a variety of natural language
understanding tasks, such as text classification, reading comprehension, etc. However, how …

The virtual-to-real paradigm, ie, training models on virtual data and then applying them to
solve real-world problems, has attracted more and more attention from various domains by …

Abstract Pose Guided Person Image Generation (PGPIG) is the task of transforming a
person image from the source pose to a given target pose. Most of the existing methods only …