One critical difference between the strategy of<cite> [12]</cite> and ours is that they concatenate all features to train a regressor, while we take a different approach. Although simply combining all features is straightforward, it can lead to noise insertion, given that the features used greatly contrast among them (e.g. word embeddings and word length). Instead, we adopted a more elegant solution, called Multi-View Learning [19] .