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        <title>Impact of ASR Transcriptions on French Spoken Coreference Resolution</title>
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            <namePart type="given">Michal</namePart>
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    <abstract>This study introduces a new ASR-transcribed coreference corpus for French and explores the transferability of coreference resolution models from human-transcribed to ASR-transcribed data. Given the challenges posed by differences in text characteristics and errors introduced by ASR systems, we evaluate model performance using newly constructed parallel human-ASR silver training and gold validation datasets. Our findings show a decline in performance on ASR data for models trained on manual transcriptions. However, combining silver ASR data with gold manual data enhances model robustness. Through detailed error analysis, we observe that models emphasizing recall are more resilient to ASR-induced errors compared to those focusing on precision. The resulting ASR corpus, along with all related materials, is freely available under the CC BY-NC-SA 4.0 license at: https://github.com/ina-foss/french-asr-coreference.</abstract>
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%0 Conference Proceedings
%T Impact of ASR Transcriptions on French Spoken Coreference Resolution
%A Milintsevich, Kirill
%Y Ogrodniczuk, Maciej
%Y Novak, Michal
%Y Poesio, Massimo
%Y Pradhan, Sameer
%Y Ng, Vincent
%S Proceedings of the Eighth Workshop on Computational Models of Reference, Anaphora and Coreference
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%F milintsevich-2025-impact
%X This study introduces a new ASR-transcribed coreference corpus for French and explores the transferability of coreference resolution models from human-transcribed to ASR-transcribed data. Given the challenges posed by differences in text characteristics and errors introduced by ASR systems, we evaluate model performance using newly constructed parallel human-ASR silver training and gold validation datasets. Our findings show a decline in performance on ASR data for models trained on manual transcriptions. However, combining silver ASR data with gold manual data enhances model robustness. Through detailed error analysis, we observe that models emphasizing recall are more resilient to ASR-induced errors compared to those focusing on precision. The resulting ASR corpus, along with all related materials, is freely available under the CC BY-NC-SA 4.0 license at: https://github.com/ina-foss/french-asr-coreference.
%U https://aclanthology.org/2025.crac-1.8/
%P 85-94

Markdown (Informal)

[Impact of ASR Transcriptions on French Spoken Coreference Resolution](https://aclanthology.org/2025.crac-1.8/) (Milintsevich, CRAC 2025)

• Impact of ASR Transcriptions on French Spoken Coreference Resolution (Milintsevich, CRAC 2025)

ACL

• Kirill Milintsevich. 2025. Impact of ASR Transcriptions on French Spoken Coreference Resolution. In Proceedings of the Eighth Workshop on Computational Models of Reference, Anaphora and Coreference, pages 85–94, Suzhou, China. Association for Computational Linguistics.