Our new X account is live! Follow @wizwand_team for updates
WorkDL logo mark

Politeness Transfer: A Tag and Generate Approach

About

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.

Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabas Poczos, Graham Neubig, Yiming Yang, Ruslan Salakhutdinov, Alan W Black, Shrimai Prabhumoye• 2020

Related benchmarks

TaskDatasetResultRank
Sentiment AnalysisYelp Reviews (Out-of-domain)
Accuracy81.3
13
Sentiment AnalysisSemeval Task B Twitter 2017 (Out-of-domain)
Accuracy79.3
10
Sentiment AnalysisAmazon Reviews (Out-of-domain)
Accuracy74.3
10
Text editingYelp (test)
Sentiment Accuracy88
9
Text editingAmazon (test)
Sentiment Accuracy65
8
Showing 5 of 5 rows

Other info

Follow for update