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Politeness Transfer: A Tag and Generate Approach

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arxiv 2004.14257 v2 pith:7CTRDID3 submitted 2020-04-29 cs.CL

classification cs.CL
keywords transferpolitenessstyleaccuracycontentevaluationsgeneratemeaning
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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.

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Cited by 2 Pith papers

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    The disconnected quark-loop contribution to the pion transition form factor has the same sign as the connected contribution, at roughly 1% of its size, as calculated on one N_f=2+1 clover ensemble.

  2. ProxyLLM : LLM-Driven Framework for Customer Support Through Text-Style Transfer

    cs.HC 2024-12 conditional novelty 4.0 of 10

    ProxyLLM uses an LLM to rewrite emotionally charged customer messages into neutral or positive tones, aiming to shield customer service agents from abuse without losing the content of the request.

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