Injecting Relational Structural Representation in Neural Networks for\n Question Similarity

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