Relying on the vast amounts of training data and unprecedented computing power, neural MT (NMT) models can now afford to access the complete information available anywhere in the source sentence and automatically learn which piece is useful at which stage of producing the output text.

In line with these advances, the field of MT has shifted to the use of deep-learning neural-based methods 8, 9, 10, 11, which replaced previous approaches, such as rule-based systems 12 or statistical phrase-based methods 13, 14. There are also other challenges in recent MT research such as gender bias 4 or unsupervised MT 5, which are mostly orthogonal to the present work.ĭeep learning transformed multiple fields in the recent years, ranging from computer vision 6 to artificial intelligence in games 7. For these reasons, the level of human translation has been thought to be the upper bound of the achievable performance 3. Among key complications is the rich morphology in the source and especially in the target language 2. The main challenges faced by MT systems are correct resolution of the inherent ambiguity of language in the source text, and adequately expressing its intended meaning in the target language (translation adequacy) in a well-formed and fluent way (translation fluency).

However, achieving major success remained elusive, in spite of the unwavering efforts of the machine translation (MT) research over the last 70 years. The idea of using computers for translation of natural languages is as old as computers themselves 1.

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