Counterfactual Learning for Machine Translation: Degeneracies and Solutions
Machine Learning
2017-12-15 v3 Computation and Language
Machine Learning
Abstract
Counterfactual learning is a natural scenario to improve web-based machine translation services by offline learning from feedback logged during user interactions. In order to avoid the risk of showing inferior translations to users, in such scenarios mostly exploration-free deterministic logging policies are in place. We analyze possible degeneracies of inverse and reweighted propensity scoring estimators, in stochastic and deterministic settings, and relate them to recently proposed techniques for counterfactual learning under deterministic logging.
Keywords
Cite
@article{arxiv.1711.08621,
title = {Counterfactual Learning for Machine Translation: Degeneracies and Solutions},
author = {Carolin Lawrence and Pratik Gajane and Stefan Riezler},
journal= {arXiv preprint arXiv:1711.08621},
year = {2017}
}
Comments
Workshop "From 'What If?' To 'What Next?'" at the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA