Learning an Executable Neural Semantic Parser

Understand

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.