TitleParallel Data Augmentation for Formality Style Transfer
AuthorsZhang, Yi
Ge, Tao
Sun, Xu
AffiliationPeking Univ, Sch EECS, MOE Key Lab Computat Linguist, Beijing, Peoples R China
Microsoft Res Asia, Beijing, Peoples R China
Microsoft Res, Redmond, WA USA
Issue Date2020
Publisher58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020)
AbstractThe main barrier to progress in the task of Formality Style Transfer is the inadequacy of training data. In this paper, we study how to augment parallel data and propose novel and simple data augmentation methods for this task to obtain useful sentence pairs with easily accessible models and systems. Experiments demonstrate that our augmented parallel data largely helps improve formality style transfer when it is used to pre-train the model, leading to the state-of-the-art results in the GYAFC benchmark dataset.
URIhttp://hdl.handle.net/20.500.11897/592033
ISBN978-1-952148-25-5
IndexedCPCI-S(ISTP)
Appears in Collections:信息科学技术学院
计算语言学教育部重点实验室

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