Title: Counterfactual Learning for Machine Translation: Degeneracies and Solutions,
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.