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 …

The field of machine translation (MT), the automatic translation of written text from one
natural language into another, has experienced a major paradigm shift in recent years …

We present a survey covering the state of the art in low-resource machine translation (MT)
research. There are currently around 7,000 languages spoken in the world and almost all …

An effective method to improve neural machine translation with monolingual data is to
augment the parallel training corpus with back-translations of target language sentences …

In spite of the recent success of neural machine translation (NMT) in standard benchmarks,
the lack of large parallel corpora poses a major practical problem for many language pairs …

We present iterative back-translation, a method for generating increasingly better synthetic
parallel data from monolingual data to train neural machine translation systems. Our …

It has been shown that the performance of neural machine translation (NMT) drops starkly in
low-resource conditions, underperforming phrase-based statistical machine translation …

Neural machine translation (NMT) is a deep learning based approach for machine
translation, which outperforms traditional statistical machine translation (SMT) and yields the …

Recent work in Neural Machine Translation (NMT) has shown significant quality gains from
noised-beam decoding during back-translation, a method to generate synthetic parallel …

We examine how various types of noise in the parallel training data impact the quality of
neural machine translation systems. We create five types of artificial noise and analyze how …