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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 ofquestions (few thousands) as gold standard (GS) training data is typicallyscarce, (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 SemEvalquestion similarity datasets show that NNs trained with our approach can learnmore accurate models, especially after fine tuning on GS.

Injecting Relational Structural Representation in Neural Networks for Question Similarity