The generation of energy from renewable sources is subjected to very dynamic changes in
environmental parameters and asset operating conditions. This is a very relevant issue to be …

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 …

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

Recently, non-recurrent architectures (convolutional, self-attentional) have outperformed
RNNs in neural machine translation. CNNs and self-attentional networks can connect distant …

Neural machine translation systems have become state-of-the-art approaches for
Grammatical Error Correction (GEC) task. In this paper, we propose a copy-augmented …

Much recent effort has been invested in non-autoregressive neural machine translation,
which appears to be an efficient alternative to state-of-the-art autoregressive machine …

Previously, neural methods in grammatical error correction (GEC) did not reach state-of-the-
art results compared to phrase-based statistical machine translation (SMT) baselines. We …

The dream of automatic language translation is now closer thanks to recent advances in the
techniques that underpin statistical machine translation. This class-tested textbook from an …

Non-autoregressive neural machine translation (NAMT) has received increasing attention
recently in virtue of its promising acceleration paradigm for fast decoding. However, these …

This paper describes the University of Edinburgh's submissions to the WMT17 shared news
translation and biomedical translation tasks. We participated in 12 translation directions for …