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Injecting Relational Structural Representation in Neural Networks for Question Similarity

Research paper by Antonio Uva, Daniele Bonadiman, Alessandro Moschitti

Indexed on: 20 Jun '18Published on: 20 Jun '18Published in: arXiv - Computer Science - Computation and Language



Abstract

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.