A Survey on Representing Linguistic Style: Challenges and Opportunities

ACL ARR 2026 January Submission238 Authors

Keywords: style representation, representation learning, style analysis, linguistic style, style in language modeling

Abstract: Although representation learning has transformed semantic modeling in NLP, representations of linguistic style remain underexplored–partly due to conflicting definitions of style within and beyond NLP or unclear immediate advantages of separate style representations. In this survey, we provide an overview of style conceptualizations across different research fields with a focus on NLP and (socio-)linguistics and suggest a working definition of style for practitioners. Then, we review methods for creating and evaluating style representations. We conclude by discussing how style representations can make crucial contributions to the modern NLP pipeline (e.g., in dataset curation or evaluation) and to the application of NLP methods in other fields. Throughout our survey, we sketch pressing open research questions in the landscape of style representations, emphasizing the need for better evaluation approaches and more comprehensive style representations.

Paper Type: Long

Research Area: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Research Area Keywords: representation learning, style analysis, style generation

Contribution Types: Position papers, Surveys

Languages Studied: English

Submission Number: 238