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Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
This paper introduces a new task of politeness transfer which involves converting non-polite sentences to polite sentences while preserving the meaning. We also provide a dataset of more than 1.39 million instances automatically labeled for politeness to encourage benchmark evaluations on this new task. We design a tag and generate pipeline that identifies stylistic attributes and subsequently generates a sentence in the target style while preserving most of the source content. For politenessdoi:10.18653/v1/2020.acl-main.169 fatcat:surqwmyupbbl5kbym7n4ximudq