Counterfactual Learning from Human Proofreading Feedback for Semantic Parsing
Abstract
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.
Cite
@article{arxiv.1811.12239,
title = {Counterfactual Learning from Human Proofreading Feedback for Semantic Parsing},
author = {Carolin Lawrence and Stefan Riezler},
journal= {arXiv preprint arXiv:1811.12239},
year = {2018}
}
Comments
"Learning by Instruction" Workshop at the 32nd Conference on Neural Information Processing Systems (NIPS 2018), Montr\'eal, Canada. arXiv admin note: substantial text overlap with arXiv:1805.01252