A Survey on Representing Linguistic Style: Challenges and Opportunities
Anna Wegmann, Cristina Aggazzotti, Rafael Rivera Soto, Dong Nguyen, Sub Natural Language Processing · Utrecht University Repository (Utrecht University) · 2026
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 \mbox{(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.