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    <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 in NLP and (socio-)linguistics and suggest a definition of style for practitioners. Guided by this definition, we then review methods for creating and evaluating predefined and neural 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. We curate relevant datasets, models, and tools on our companion webpage at https://annawegmann.github.io/StyleSurvey/. Throughout, we provide practical guidance, identify open research questions with concrete recommendations for addressing them and issue calls to action on critical gaps in current work.</abstract>
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%0 Conference Proceedings
%T A Survey on Representing Linguistic Style: Challenges and Opportunities
%A Wegmann, Anna
%A Aggazzotti, Cristina
%A Rivera Soto, Rafael Alberto
%A Nguyen, Dong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wegmann-etal-2026-survey
%X 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 in NLP and (socio-)linguistics and suggest a definition of style for practitioners. Guided by this definition, we then review methods for creating and evaluating predefined and neural 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. We curate relevant datasets, models, and tools on our companion webpage at https://annawegmann.github.io/StyleSurvey/. Throughout, we provide practical guidance, identify open research questions with concrete recommendations for addressing them and issue calls to action on critical gaps in current work.
%U https://aclanthology.org/2026.emnlp-main.295/
%P 6772-6806

Markdown (Informal)

[A Survey on Representing Linguistic Style: Challenges and Opportunities](https://aclanthology.org/2026.emnlp-main.295/) (Wegmann et al., EMNLP 2026)

• A Survey on Representing Linguistic Style: Challenges and Opportunities (Wegmann et al., EMNLP 2026)

ACL

• Anna Wegmann, Cristina Aggazzotti, Rafael Alberto Rivera Soto, and Dong Nguyen. 2026. A Survey on Representing Linguistic Style: Challenges and Opportunities. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 6772–6806, Budapest, Hungary. Association for Computational Linguistics.