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Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer

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arxiv 2110.10668 v1 pith:WTKABZ34 submitted 2021-10-20 cs.CL

classification cs.CL
keywords evaluationtransferstyleautomaticformalitymetricsbeenfocus
verification ladder T0 review T1 audit T2 compute T3 formal
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While the field of style transfer (ST) has been growing rapidly, it has been hampered by a lack of standardized practices for automatic evaluation. In this paper, we evaluate leading ST automatic metrics on the oft-researched task of formality style transfer. Unlike previous evaluations, which focus solely on English, we expand our focus to Brazilian-Portuguese, French, and Italian, making this work the first multilingual evaluation of metrics in ST. We outline best practices for automatic evaluation in (formality) style transfer and identify several models that correlate well with human judgments and are robust across languages. We hope that this work will help accelerate development in ST, where human evaluation is often challenging to collect.

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  1. Absolute Evaluation Measures for Machine Learning: A Survey

    cs.LG 2025-07 unverdicted novelty 1.0 of 10

    A survey compiles bounded absolute evaluation metrics for classification, clustering, and ranking and proposes decision trees for metric selection, but several formulas are reproduced incorrectly.

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