Learning an Executable Neural Semantic Parser

by   Jianpeng Cheng, et al.

This paper describes a neural semantic parser that maps natural language utterances onto logical forms which can be executed against a task-specific environment, such as a knowledge base or a database, to produce a response. The parser generates tree-structured logical forms with a transition-based approach which combines a generic tree-generation algorithm with domain-general operations defined by the logical language. The generation process is modeled by structured recurrent neural networks, which provide a rich encoding of the sentential context and generation history for making predictions. To tackle mismatches between natural language and logical form tokens, various attention mechanisms are explored. Finally, we consider different training settings for the neural semantic parser, including a fully supervised training where annotated logical forms are given, weakly-supervised training where denotations are provided, and distant supervision where only unlabeled sentences and a knowledge base are available. Experiments across a wide range of datasets demonstrate the effectiveness of our parser.


page 1

page 2

page 3

page 4


Weakly-supervised Neural Semantic Parsing with a Generative Ranker

Weakly-supervised semantic parsers are trained on utterance-denotation p...

SKATE: A Natural Language Interface for Encoding Structured Knowledge

In Natural Language (NL) applications, there is often a mismatch between...

Simpler Context-Dependent Logical Forms via Model Projections

We consider the task of learning a context-dependent mapping from uttera...

Look-up and Adapt: A One-shot Semantic Parser

Computing devices have recently become capable of interacting with their...

Semantic Construction Grammar: Bridging the NL / Logic Divide

In this paper, we discuss Semantic Construction Grammar (SCG), a system ...

A Survey on Semantic Parsing

A significant amount of information in today's world is stored in struct...

Symbolic Priors for RNN-based Semantic Parsing

Seq2seq models based on Recurrent Neural Networks (RNNs) have recently r...