Human-assisted Neural Machine Translation: Harnessing Human Feedback for Machine Translation

Abstract

Neural machine translation (NMT) is a promising approach to the task of machine translation that has led to state-of-the-art results in many settings. However, NMT translations are still far from sufficient for many practical purposes. For this reason, there is a robust body of ongoing research on how to improve NMT systems with human feedback. This feedback can take many forms, including interactive-predictive NMT, post-editing of NMT output, and soliciting ratings or corrections of translations for the purposes of online learning. While these approaches are often effective, they are also often very time consuming and expensive. For that reason, there is important research into the question of how best to ensure that any human effort is used optimally. In this thesis, we contribute to this line of work by proposing a system that learns when it should ask for human feedback on a translation. This system makes use of an existing pre-trained NMT model, and introduces an additional feedback-requester model that learns to selectively solicit feedback from a human translator on the NMT translations. This system reduces human effort by directing attention to the most problematic sentences in a document, and the feedback-requester model itself is updated according to the translator's feedback. We also experiment with two active learning (AL) strategies for the feedback-requester model, and present a range of experiments simulating human translator use of the system and show the results over time.