Abstract
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.
Abstract (translated)
神经机器翻译（NMT）系统放大了训练数据中存在的词汇偏见，导致输出翻译的语言人为贫乏。这些语言层面的特征使得自动翻译与原语言中最初撰写的内容及人工翻译有所不同，从而阻碍了它们在例如创建评估数据集方面的实用性。试图提高NMT自然度的努力可能会在内容保真方面不足，即增加词汇多样性是以牺牲翻译准确性为代价的。受人类反馈强化学习框架的启发，我们提出了一种新方法，奖励自然度和内容保真度两方面。我们从多个角度进行实验以生成更自然的翻译，旨在减少机器和人工翻译的语言特征差异。我们在英语到荷兰语的文学翻译上评估了我们的方法，并发现我们最佳模型生成的翻译在词汇丰富性方面更强且更多地展现了人类撰写语言的特点，同时没有牺牲翻译准确性。
URL
https://arxiv.org/abs/2412.08473
