Counterfactual Learning from Human Proofreading Feedback for Semantic Parsing

Understand

In semantic parsing for question-answering, it is often too expensive to collect gold parses or even gold answers as supervision signals.

• We propose to convert model outputs into a set of human-understandable statements which allow non-expert users to act as proofreaders, providing error markings as learning signals to the parser.
• Because model outputs were suggested by a historic system, we operate in a counterfactual, or off-policy, learning setup.
• We introduce new estimators which can effectively leverage the given feedback and which avoid known degeneracies in counterfactual learning, while still being applicable to stochastic gradient optimization for neural semantic parsing.