Learning to Detect Unacceptable Machine Translations for Downstream Tasks

• URL: http://arxiv.org/abs/2005.03925v1
• Date: Fri, 8 May 2020 09:37:19 GMT
• Title: Learning to Detect Unacceptable Machine Translations for Downstream Tasks
• Authors: Meng Zhang, Xin Jiang, Yang Liu, Qun Liu
• Abstract summary: 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. We conduct experiments to demonstrate the effectiveness of our framework for a range of downstream tasks and translation models.
• Score: 33.07594909221625
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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