论文标题
学会检测下游任务的不可接受的机器翻译
Learning to Detect Unacceptable Machine Translations for Downstream Tasks
论文作者
论文摘要
近年来，机器翻译领域取得了巨大进展。即使翻译质量得到了显着提高，目前的系统仍无法为各种可能的用例产生均匀可接受的机器翻译。在这项工作中，我们将机器翻译放入跨语言管道中，并引入下游任务以定义机器翻译的特定任务可接受性。这使我们能够利用并行数据自动生成大规模的可接受性注释，这反过来又有助于学习下游任务的可接受性检测器。我们进行实验，以证明我们框架对一系列下游任务和翻译模型的有效性。
The field of machine translation has progressed tremendously in recent years. Even though the translation quality has improved significantly, current systems are still unable to produce uniformly acceptable machine translations for the variety of possible use cases. In this work, we put machine translation in a cross-lingual pipeline and introduce downstream tasks to define task-specific acceptability of machine translations. This allows us to leverage parallel data to automatically generate acceptability annotations on a large scale, which in turn help to learn acceptability detectors for the downstream tasks. We conduct experiments to demonstrate the effectiveness of our framework for a range of downstream tasks and translation models.
