Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing
Bo Chen, Le Sun, Xianpei Han · 2018
This paper proposes a neural semantic parsing approach -Sequence-to-Action, which models semantic parsing as an endto-end semantic graph generation process.Our method simultaneously leverages the advantages from two recent promising directions of semantic parsing.Firstly, our model uses a semantic graph to represent the meaning of a sentence, which has a tight-coupling with knowledge bases.Secondly, by leveraging the powerful representation learning and prediction ability of neural network models, we propose a RNN model which can effectively map sentences to action sequences for semantic graph generation.Experiments show that our method achieves state-of-the-art performance on OVERNIGHT dataset and gets competitive performance on GEO and ATIS datasets.