Paper tackles morphology simplification for Chinese-Spanish machine translation.
problem Challenges in morphology generation for unbalanced languages in machine translation.
method Proposes a new neural architecture for morphological simplification, combining embedding, convolutional, and recurrent neural network layers.
result Obtains over 98% accuracy in gender classification, over 93% in number classification, and an overall translation improvement of 0.7 METEOR.