Neural machine translation (NMT) systems amplify lexical biases present in their training data, leading to artificially impoverished language in output translations. These language-level characteristics render automatic translations different from text originally written in a language and human translations, which hinders their usefulness in for example creating evaluation datasets. Attempts to increase naturalness in NMT can fall short in terms of content preservation, where increased lexical diversity comes at the cost of translation accuracy. Inspired by the reinforcement learning from human feedback framework, we introduce a novel method that rewards both naturalness and content preservation. We experiment with multiple perspectives to produce more natural translations, aiming at reducing machine and human translationese. We evaluate our method on English-to-Dutch literary translation, and find that our best model produces translations that are lexically richer and exhibit more properties of human-written language, without loss in translation accuracy.

翻译：神经机器翻译（NMT）系统会放大训练数据中存在的词汇偏差，导致输出译文在语言层面显得贫乏。这些语言层面的特征使得自动翻译文本与源语言原创文本及人工翻译文本存在差异，从而限制了其应用价值，例如在构建评估数据集时。现有提升NMT自然性的尝试往往难以兼顾内容保真度，词汇多样性的提升常以翻译准确性为代价。受人类反馈强化学习框架的启发，我们提出一种同时奖励自然性与内容保真的新方法。我们通过多视角实验以生成更自然的翻译，旨在减少机器翻译体与人工翻译体特征。我们在英译荷兰语文学翻译任务上评估了该方法，发现最优模型生成的译文词汇更丰富，且展现出更多人类书写语言的特征，同时未损失翻译准确性。