2 research outputs found
Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing
This paper proposes a neural semantic parsing approach -- Sequence-to-Action,
which models semantic parsing as an end-to-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.Comment: Accepted as ACL 2018 long pape
From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decoding
Semantic parsing is challenging due to the structure gap and the semantic gap
between utterances and logical forms. In this paper, we propose an unsupervised
semantic parsing method - Synchronous Semantic Decoding (SSD), which can
simultaneously resolve the semantic gap and the structure gap by jointly
leveraging paraphrasing and grammar constrained decoding. Specifically, we
reformulate semantic parsing as a constrained paraphrasing problem: given an
utterance, our model synchronously generates its canonical utterance and
meaning representation. During synchronous decoding: the utterance paraphrasing
is constrained by the structure of the logical form, therefore the canonical
utterance can be paraphrased controlledly; the semantic decoding is guided by
the semantics of the canonical utterance, therefore its logical form can be
generated unsupervisedly. Experimental results show that SSD is a promising
approach and can achieve competitive unsupervised semantic parsing performance
on multiple datasets.Comment: Accepted by ACL 202