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        <title>Perceived Professionalism in Financial Inquiry: An Interpretable and Empirically Grounded Linguistic Modeling of Textual Preference</title>
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        <namePart type="given">Chung-Chi</namePart>
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    <abstract>Professionalism is a central yet underdefined aspect of expert communication. This study investigates whether perceived professionalism can be modeled computationally in financial Q A contexts, and whether non-expert preferences provide a meaningful basis for its annotation. We compile a mixed dataset of real and synthetically generated questions from earnings calls, annotated with human judgments of perceived professionalism and linguistically motivated features. Regression and classification experiments show that these features effectively capture evaluators’ preferences, enabling simple models to outperform both text-only and LLM baselines. The results demonstrate that interpretable, conceptually grounded linguistic features can rival more complex architectures while providing greater transparency, and support the value of linguistic awareness in NLP. More broadly, the study shows that modeling reader preferences can offers insights for the generation of professional questions.</abstract>
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%0 Conference Proceedings
%T Perceived Professionalism in Financial Inquiry: An Interpretable and Empirically Grounded Linguistic Modeling of Textual Preference
%A D’Agostino, Giulia
%A Chen, Chung-Chi
%Y Huang, Jimin
%Y Lee, Yongjae
%Y Ananiadou, Sophia
%Y Chen, Hsin-Hsi
%Y Takamura, Hiroya
%Y Chen, Chung-Chi
%S Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-466-8
%F d-agostino-chen-2026-perceived
%X Professionalism is a central yet underdefined aspect of expert communication. This study investigates whether perceived professionalism can be modeled computationally in financial Q A contexts, and whether non-expert preferences provide a meaningful basis for its annotation. We compile a mixed dataset of real and synthetically generated questions from earnings calls, annotated with human judgments of perceived professionalism and linguistically motivated features. Regression and classification experiments show that these features effectively capture evaluators’ preferences, enabling simple models to outperform both text-only and LLM baselines. The results demonstrate that interpretable, conceptually grounded linguistic features can rival more complex architectures while providing greater transparency, and support the value of linguistic awareness in NLP. More broadly, the study shows that modeling reader preferences can offers insights for the generation of professional questions.
%U https://aclanthology.org/2026.finnlp-1.43/
%P 524-548

Markdown (Informal)

[Perceived Professionalism in Financial Inquiry: An Interpretable and Empirically Grounded Linguistic Modeling of Textual Preference](https://aclanthology.org/2026.finnlp-1.43/) (D’Agostino & Chen, FinNLP 2026)

• Perceived Professionalism in Financial Inquiry: An Interpretable and Empirically Grounded Linguistic Modeling of Textual Preference (D’Agostino & Chen, FinNLP 2026)

ACL

• Giulia D’Agostino and Chung-Chi Chen. 2026. Perceived Professionalism in Financial Inquiry: An Interpretable and Empirically Grounded Linguistic Modeling of Textual Preference. In Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing, pages 524–548, Budapest, Hungary. Association for Computational Linguistics.