(2016) showed that the distance between the attentioninfused alignments and the ones learned by an independent alignment model can be added to the networks' training objective, resulting in an improved translation and alignment quality.
(2008), dividing the data for each inflection type into five folds, each consisting of 500 training, 1000 development and 1000 test examples.