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arXiv · 1808.07624

Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model

Kun Xu·Lingfei Wu·Zhiguo Wang·Mo Yu·Liwei Chen·Vadim Sheinin
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Abstract

Existing neural semantic parsers mainly utilize a sequence encoder, i.e., a sequential LSTM, to extract word order features while neglecting other valuable syntactic information such as dependency graph or constituent trees. In this paper, we first propose to use the \textit{syntactic graph} to represent three types of syntactic information, i.e., word order, dependency and constituency features. We further employ a graph-to-sequence model to encode the syntactic graph and decode a logical form. Experimental results on benchmark datasets show that our model is comparable to the state-of-the-art on Jobs640, ATIS and Geo880. Experimental results on adversarial examples demonstrate the robustness of the model is also improved by encoding more syntactic information.

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Kun Xu, Lingfei Wu, Zhiguo Wang, Mo Yu, Liwei Chen, Vadim Sheinin. 2018-08-23. Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model. https://arxiv.org/abs/1808.07624

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