Improving Machine Translation with Human Feedback: An Exploration of Quality Estimation as a Reward Model

arXiv:2401.12873v2 Announce Type: replace
Abstract: Insufficient modeling of human preferences within the reward model is a major obstacle for leveraging human feedback to improve translation quality. Fortunately, quality estimation (QE), which predicts the quality of a given translation without reference, has achieved impressive alignment with human evaluations in the last two years. In this work, we investigate the potential of employing the QE model as the reward model (the QE-based reward model) to predict human preferences for feedback training. We …

arxiv cs.ai cs.cl exploration feedback human human feedback machine machine translation quality reward model translation type