Incorporating personal preference is crucial in advanced machine translation tasks. Despite the recent advancement of machine translation, it remains a demanding task to properly reflect personal style. In this paper, we introduce a personalized automatic post-editing framework to address this challenge, which effectively generates sentences considering distinct personal behaviors. To build this framework, we first collect post-editing data that connotes the user preference from a live machine translation system. Specifically, real-world users enter source sentences for translation and edit the machine-translated outputs according to the user's preferred style. We then propose a model that combines a discriminator module and user-specific parameters on the APE framework. Experimental results show that the proposed method outperforms other baseline models on four different metrics (i.e., BLEU, TER, YiSi-1, and human evaluation).

翻译：摘要：融入个人偏好是高级机器翻译任务中的关键。尽管机器翻译近期取得了进展，但恰当反映个人风格仍是一项艰巨任务。本文提出了一种个性化自动后编辑框架来应对这一挑战，该框架能有效生成考虑不同个人行为的句子。为构建此框架，我们首先从实时机器翻译系统中收集蕴含用户偏好的后编辑数据。具体而言，真实用户输入源句进行翻译，并根据其偏好风格编辑机器翻译输出。随后，我们提出一种模型，在自动后编辑框架上结合了判别器模块和用户特定参数。实验结果表明，所提方法在四项不同指标（即BLEU、TER、YiSi-1及人工评估）上均优于其他基线模型。