Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning

Understand

Neural semantic parsing has achieved impressive results in recent years, yet its success relies on the availability of large amounts of supervised data.

• Our goal is to learn a neural semantic parser when only prior knowledge about a limited number of simple rules is available, without access to either annotated programs or execution results.
• Our approach is initialized by rules, and improved in a back-translation paradigm using generated question-program pairs from the semantic parser and the question generator.
• A phrase table with frequent mapping patterns is automatically derived, also updated as training progresses, to measure the quality of generated instances.