23,265 research outputs found
Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback
Machine translation is a natural candidate problem for reinforcement learning
from human feedback: users provide quick, dirty ratings on candidate
translations to guide a system to improve. Yet, current neural machine
translation training focuses on expensive human-generated reference
translations. We describe a reinforcement learning algorithm that improves
neural machine translation systems from simulated human feedback. Our algorithm
combines the advantage actor-critic algorithm (Mnih et al., 2016) with the
attention-based neural encoder-decoder architecture (Luong et al., 2015). This
algorithm (a) is well-designed for problems with a large action space and
delayed rewards, (b) effectively optimizes traditional corpus-level machine
translation metrics, and (c) is robust to skewed, high-variance, granular
feedback modeled after actual human behaviors.Comment: 11 pages, 5 figures, In Proceedings of Empirical Methods in Natural
Language Processing (EMNLP) 201
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