Understanding and Improving Morphological Learning in the Neural Machine Translation Decoder

IJCNLP 2017 Fahim DalviNadir DurraniHassan SajjadYonatan BelinkovStephan Vogel

End-to-end training makes the neural machine translation (NMT) architecture simpler, yet elegant compared to traditional statistical machine translation (SMT). However, little is known about linguistic patterns of morphology, syntax and semantics learned during the training of NMT systems, and more importantly, which parts of the architecture are responsible for learning each of these phenomenon... (read more)

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