In terms of performing distributed representation learning for output variables, our proposed model shares similarity with the structured output representation learning approach developed by Srikumar and Manning (2014), which extends the structured support vector machines to simultaneously learn the prediction model and the distributed representations of the output labels.
, s * T } using the standard Viterbi algorithm (Rabiner and Juang, 1986) for each labeled source training sentence and each target test sentence.