Injecting Relational Structural Representation in Neural Networks for Question Similarity

Antonio Uva, Daniele Bonadiman, Alessandro Moschitti
2018 Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)  
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
more » ... 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.
doi:10.18653/v1/p18-2046 dblp:conf/acl/UvaBM18 fatcat:ir53z7vlpfhwhctqqtcleu5oey