Auto-Adaptive Learning from Weak Feedback for Interactive Lecture Translation

2017-2020

Research Grant

Summary: High-quality machine translation requires the interaction with human translators, either in form of post-edits or interactive translation prediction. The high cost and required expertise of professional translators calls for a scenario where machine translation systems can be learned from weaker feedback that is elicitable from laymen users. Similar to computational advertising, where systems are adapted from user clicks, we attempt to learn machine translation from bandit feedback in form of judgements on the quality of a predicted translation without requiring a post-edit or a gold-standard translation.