With the advent of deep learning, the state of art performance for various semantic NLP tasks has seen a significant boost (Collobert and Weston, 2008).
most of these techniques are data-hungry, and require a large number of sufficiently diverse labeled training samples, e.g., for QA, training samples should not only encompass an entire range of possible questions but also have them in sufficient quantity (Bordes et al., 2015).