Pieces of eight: 8-bit neural machine translation
Jerry Quinn, Miguel Ballesteros
NAACL 2018
Multilingual machine translation addresses the task of translating between multiple source and target languages. We propose task-specific attention models, a simple but effective technique for improving the quality of sequence-to-sequence neural multilingual translation. Our approach seeks to retain as much of the parameter sharing generalization of NMT models as possible, while still allowing for language-specific specialization of the attention model to a particular language-pair or task. Our experiments on four languages of the Europarl corpus show that using a target-specific model of attention provides consistent gains in translation quality for all possible translation directions, compared to a model in which all parameters are shared. We observe improved translation quality even in the (extreme) low-resource zero-shot translation directions for which the model never saw explicitly paired parallel data.
Jerry Quinn, Miguel Ballesteros
NAACL 2018
Ryosuke Kohita, Hiroshi Noji, et al.
COLING 2018
Xiaoqiang Luo, Radu Florian, et al.
NAACL-HLT 2009
Adrià De Gispert, Graeme Blackwood, et al.
Machine Translation