Politeness Transfer: A Tag and Generate Approach
This addresses the problem of generating polite text for applications like chatbots or customer service, though it is incremental as it builds on existing style transfer methods.
The paper tackles the task of politeness transfer, converting non-polite sentences to polite ones while preserving meaning, and introduces a dataset of over 1.39 instances for benchmarking. Their tag-and-generate model outperforms state-of-the-art methods on automatic metrics for content preservation and achieves comparable or better style transfer accuracy across six tasks, including politeness.
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 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 politeness as well as five other transfer tasks, our model outperforms the state-of-the-art methods on automatic metrics for content preservation, with a comparable or better performance on style transfer accuracy. Additionally, our model surpasses existing methods on human evaluations for grammaticality, meaning preservation and transfer accuracy across all the six style transfer tasks. The data and code is located at https://github.com/tag-and-generate.