(Male, Bachelor) and (Female, Ph.D) have different connotations: Parallelly Annotated Stylistic Language Dataset with Multiple Personas

By Dongyeop Kang, Varun Prashant Gangal, Eduard H. Hovy. 2019

22 citations recorded by the source corpus.

Stylistic variation in text needs to be studied with different aspects including the writer’s personal traits, interpersonal relations, rhetoric, and more. Despite recent attempts on computational modeling of the variation, the lack of parallel corpora of style language makes it difficult to systematically control the stylistic change as well as evaluate such models. We release PASTEL, the parallel and annotated stylistic language dataset, that contains ~41K parallel sentences (8.3K parallel stories) annotated across different personas. Each persona has different styles in conjunction: gender, age, country, political view, education, ethnic, and time-of-writing. The dataset is collected from human annotators with solid control of input denotation: not only preserving original meaning between text, but promoting stylistic diversity to annotators. We test the dataset on two interesting applications of style language, where PASTEL helps design appropriate experiment and evaluation. First, in predicting a target style (e.g., male or female in gender) given a text, multiple styles of PASTEL make other external style variables controlled (or fixed), which is a more accurate experimental design. Second, a simple supervised model with our parallel text outperforms the unsupervised models using nonparallel text in style transfer. Our dataset is publicly available.

Subfields: Artificial Intelligence

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Citations in the corpus

Theories of “Gender” in NLP Bias Research (2022, background): “13176v1 [33] Amanda Cercas Curry and Verena Rieser.”
GenAug: Data Augmentation for Finetuning Text Generators (2020, background): “Lastly, there is potential in exploring data augmentation for other domains such as dialogue and related tasks such as style transfer (Kang et al., 2019), and investigating interesting aspects of it such as dialogue personalization (Li et al.”
Does It Capture STEL? A Modular, Similarity-based Linguistic Style Evaluation Framework (2021, methodology): “Binary and more fine-grained style classification has been employed on word, text fragment as well as document level (Danescu-Niculescu-Mizil et al., 2013; Pavlick and Nenkova, 2015; Preoţiuc- Pietro et al., 2016; Pavlick and Tetreault, 2016; Kang et al., 2019).”