Injecting Relational Structural Representation in Neural Networks for Question Similarity
TL
TL;DR
This paper proposes to inject structural representations in NNs by learning a model with Tree Kernels on relatively few pairs of questions as gold standard training data is typically scarce, and predicting labels on a very large corpus of question pairs.
Abstract
Effectively using full syntactic parsing information in Neural Networks (NNs) for solving 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 a model with 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 using our approach can learn more accurate models, especially after fine tuning on GS.
References33 items
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The Bottleneck
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SemEval-2016 Task 3: Community Question Answering
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Deep Learning in Semantic Kernel Spaces
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SemEval-2017 Task 3: Community Question Answering
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