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.
the corresponding distributed state representation vectors can be obtained as We then train a supervised NLP system (e.g., POS tagging or NP chunking) on the labeled source training sentences using the distributed state representations as augmenting input features and perform prediction on the augmented test sentences.