Injecting Relational Structural Representation in Neural Networks for Question Similarity

ACL 2018 • Antonio Uva • Daniele Bonadiman • Alessandro Moschitti

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... (read more)

PDF Abstract ACL 2018 PDF ACL 2018 Abstract

Results from the Paper

Edit

Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

METHOD	TYPE
SVM	Non-Parametric Classification

METHOD

TYPE

SVM

Non-Parametric Classification

Injecting Relational Structural Representation in Neural Networks for Question Similarity

Code

Add Remove Mark official

Tasks

Add Remove

Datasets

Results from the Paper

Edit

Methods used in the Paper

Add Remove

Edit Social Preview

Injecting Relational Structural Representation in Neural Networks for Question Similarity Edit social preview

Code Edit Add Remove Mark official

Tasks Edit Add Remove

Datasets Edit

Results from the Paper Edit

Methods used in the Paper Edit Add Remove

Injecting Relational Structural Representation in Neural Networks for Question Similarity

Code

Add Remove Mark official

Tasks

Add Remove

Datasets

Results from the Paper

Edit

Methods used in the Paper

Add Remove