Counterfactual Learning from Human Proofreading Feedback for Semantic Parsing
This addresses the high cost of supervision in semantic parsing for virtual personal assistants, offering a practical solution for deployment, though it is incremental in applying counterfactual learning to this domain.
The paper tackles the problem of expensive supervision in semantic parsing for question-answering by proposing a method to use human proofreading feedback as learning signals, showing that semantic parsers can be significantly improved through counterfactual learning from logged human feedback data.
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. Furthermore, we discuss how our feedback collection method can be seamlessly integrated into deployed virtual personal assistants that embed a semantic parser. Our work is the first to show that semantic parsers can be improved significantly by counterfactual learning from logged human feedback data.