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        <title>Does It Capture STEL? A Modular, Similarity-based Linguistic Style Evaluation Framework</title>
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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            <namePart type="given">Xuanjing</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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    <abstract>Style is an integral part of natural language. However, evaluation methods for style measures are rare, often task-specific and usually do not control for content. We propose the modular, fine-grained and content-controlled similarity-based STyle EvaLuation framework (STEL) to test the performance of any model that can compare two sentences on style. We illustrate STEL with two general dimensions of style (formal/informal and simple/complex) as well as two specific characteristics of style (contrac’tion and numb3r substitution). We find that BERT-based methods outperform simple versions of commonly used style measures like 3-grams, punctuation frequency and LIWC-based approaches. We invite the addition of further tasks and task instances to STEL and hope to facilitate the improvement of style-sensitive measures.</abstract>
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%0 Conference Proceedings
%T Does It Capture STEL? A Modular, Similarity-based Linguistic Style Evaluation Framework
%A Wegmann, Anna
%A Nguyen, Dong
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F wegmann-nguyen-2021-capture
%X Style is an integral part of natural language. However, evaluation methods for style measures are rare, often task-specific and usually do not control for content. We propose the modular, fine-grained and content-controlled similarity-based STyle EvaLuation framework (STEL) to test the performance of any model that can compare two sentences on style. We illustrate STEL with two general dimensions of style (formal/informal and simple/complex) as well as two specific characteristics of style (contrac’tion and numb3r substitution). We find that BERT-based methods outperform simple versions of commonly used style measures like 3-grams, punctuation frequency and LIWC-based approaches. We invite the addition of further tasks and task instances to STEL and hope to facilitate the improvement of style-sensitive measures.
%R 10.18653/v1/2021.emnlp-main.569
%U https://aclanthology.org/2021.emnlp-main.569
%U https://doi.org/10.18653/v1/2021.emnlp-main.569
%P 7109-7130

Markdown (Informal)

[Does It Capture STEL? A Modular, Similarity-based Linguistic Style Evaluation Framework](https://aclanthology.org/2021.emnlp-main.569) (Wegmann & Nguyen, EMNLP 2021)

• Does It Capture STEL? A Modular, Similarity-based Linguistic Style Evaluation Framework (Wegmann & Nguyen, EMNLP 2021)

ACL

• Anna Wegmann and Dong Nguyen. 2021. Does It Capture STEL? A Modular, Similarity-based Linguistic Style Evaluation Framework. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7109–7130, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.