  Effectively using full syntactic parsing information in Neural Networks (NNs)
to solve relational tasks, e.g., question similarity, is still an open problem.
In this paper, we propose to inject structural representations in NNs by (i)
learning an SVM model using Tree Kernels (TKs) on relatively few pairs of
questions (few thousands) as gold standard (GS) training data is typically
scarce, (ii) predicting labels on a very large corpus of question pairs, and
(iii) pre-training NNs on such large corpus. The results on Quora and SemEval
question similarity datasets show that NNs trained with our approach can learn
more accurate models, especially after fine tuning on GS.
