Interactive-Predictive Neural Machine Translation through Reinforcement and Imitation
arXiv:1907.02326
Abstract
We propose an interactive-predictive neural machine translation framework for easier model personalization using reinforcement and imitation learning. During the interactive translation process, the user is asked for feedback on uncertain locations identified by the system. Responses are weak feedback in the form of "keep" and "delete" edits, and expert demonstrations in the form of "substitute" edits. Conditioning on the collected feedback, the system creates alternative translations via constrained beam search. In simulation experiments on two language pairs our systems get close to the performance of supervised training with much less human effort.
Machine Translation Summit 2019 (MTSUMMIT XVII), Dublin, Ireland
References in corpus (5)
- Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
- Achieving Human Parity on Automatic Chinese to English News Translation
- Fast Policy Learning through Imitation and Reinforcement
- Online Learning for Neural Machine Translation Post-editing
- A Reinforcement Learning Approach to Interactive-Predictive Neural Machine Translation