We present an approach to interactive-predictive neural machine translation
that attempts to reduce human effort from three directions: Firstly, instead of
requiring humans to select, correct, or delete segments, we employ the idea of
learning from human reinforcements in form of judgments on the quality of
partial translations. Secondly, human effort is further reduced by using the
entropy of word predictions as uncertainty criterion to trigger feedback
requests. Lastly, online updates of the model parameters after every
interaction allow the model to adapt quickly. We show in simulation experiments
that reward signals on partial translations significantly improve character
F-score and BLEU compared to feedback on full translations only, while human
effort can be reduced to an average number of $5$ feedback requests for every
input.

该研究提出了一种交互式预测神经机器翻译的方法，通过人类质量判断和基于熵的不确定性准则来降低人力需求以及实现在线模型参数的更新，模拟实验表明这种方法可以显著提高翻译质量并将人力需求控制在一个较低的水平。

一种交互式预测神经机器翻译的强化学习方法

A Reinforcement Learning Approach to Interactive-Predictive Neural  Machine Translation

In this paper, we propose an adaptive algorithm that iteratively updates both
the weights and component parameters of a mixture importance sampling density
so as to optimise the importance sampling performances, as measured by an
entropy criterion. The method is shown to be applicable to a wide class of
importance sampling densities, which includes in particular mixtures of
multivariate Student t distributions. The performances of the proposed scheme
are studied on both artificial and real examples, highlighting in particular
the benefit of a novel Rao-Blackwellisation device which can be easily
incorporated in the updating scheme.

本文提出一种自适应算法，通过迭代地更新混合重要性采样密度的权重和组分参数，以优化重要性采样性能，该方法适用于广泛类别的重要性采样密度，包括特别是多元学生 t 分布的混合物，实验表明，该算法在人工和实际例子上的表现都很好，并且特别突出了一个新颖的 Rao-Blackwellisation 装置的好处，该装置可以轻松地纳入更新方案中。