论文标题
学习非单调的自动自动在人类订购中翻译的翻译后编辑
Learning Non-Monotonic Automatic Post-Editing of Translations from Human Orderings
论文作者
论文摘要
神经机器翻译的最新研究探索了灵活的生成订单，作为从左到右的一代的替代方案。但是，训练非单调模型会带来新的并发症：当有订单的组合爆炸时，如何搜索良好的订购？另外，这些自动订购与人类翻译人员的实际行为相比如何？当前的模型依靠手动建立的偏见或独自探索所有可能性。在本文中，我们分析了人类邮政编辑所产生的订单，并使用它们来训练自动编辑系统。我们将最终的系统与经过从左到右和随机的后编辑订单训练的系统进行比较。我们观察到人类倾向于遵循几乎从左到右的顺序，但是有有趣的偏差，例如宁愿从校正标点符号或动词开始。
Recent research in neural machine translation has explored flexible generation orders, as an alternative to left-to-right generation. However, training non-monotonic models brings a new complication: how to search for a good ordering when there is a combinatorial explosion of orderings arriving at the same final result? Also, how do these automatic orderings compare with the actual behaviour of human translators? Current models rely on manually built biases or are left to explore all possibilities on their own. In this paper, we analyze the orderings produced by human post-editors and use them to train an automatic post-editing system. We compare the resulting system with those trained with left-to-right and random post-editing orderings. We observe that humans tend to follow a nearly left-to-right order, but with interesting deviations, such as preferring to start by correcting punctuation or verbs.
