@inproceedings{schneider-ruiz-fabo-2024-stage,
title = "Stage Direction Classification in {F}rench Theater: Transfer Learning Experiments",
author = "Schneider, Alexia and
Ruiz Fabo, Pablo",
editor = "Bizzoni, Yuri and
Degaetano-Ortlieb, Stefania and
Kazantseva, Anna and
Szpakowicz, Stan",
booktitle = "Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024)",
month = mar,
year = "2024",
address = "St. Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.latechclfl-1.28",
pages = "278--286",
abstract = "The automatic classification of stage directions is a little explored topic in computational drama analysis, in spite of their relevance for plays{'} structural and stylistic analysis. With a view to start assessing good practices for the automatic annotation of this textual element, we developed a 13-class stage direction typology, based on annotations in the FreDraCor corpus (French-language plays), but abstracting away from their huge variability while still providing classes useful for literary research. We fine-tuned transformers-based models to classify against the typology, gradually decreasing the corpus size used for fine tuning, to compare model efficiency with reduced training data. A result comparison speaks in favour of distilled monolingual models for this task, and, unlike earlier research on German, shows no negative effects of model case-sensitivity. The results have practical relevance for computational literary studies, as comparing classification results with complementary stage direction typologies, limiting the amount of manual annotation needed to apply them, would be helpful towards a systematic study of this important textual element.",
}
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<abstract>The automatic classification of stage directions is a little explored topic in computational drama analysis, in spite of their relevance for plays’ structural and stylistic analysis. With a view to start assessing good practices for the automatic annotation of this textual element, we developed a 13-class stage direction typology, based on annotations in the FreDraCor corpus (French-language plays), but abstracting away from their huge variability while still providing classes useful for literary research. We fine-tuned transformers-based models to classify against the typology, gradually decreasing the corpus size used for fine tuning, to compare model efficiency with reduced training data. A result comparison speaks in favour of distilled monolingual models for this task, and, unlike earlier research on German, shows no negative effects of model case-sensitivity. The results have practical relevance for computational literary studies, as comparing classification results with complementary stage direction typologies, limiting the amount of manual annotation needed to apply them, would be helpful towards a systematic study of this important textual element.</abstract>
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%0 Conference Proceedings
%T Stage Direction Classification in French Theater: Transfer Learning Experiments
%A Schneider, Alexia
%A Ruiz Fabo, Pablo
%Y Bizzoni, Yuri
%Y Degaetano-Ortlieb, Stefania
%Y Kazantseva, Anna
%Y Szpakowicz, Stan
%S Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F schneider-ruiz-fabo-2024-stage
%X The automatic classification of stage directions is a little explored topic in computational drama analysis, in spite of their relevance for plays’ structural and stylistic analysis. With a view to start assessing good practices for the automatic annotation of this textual element, we developed a 13-class stage direction typology, based on annotations in the FreDraCor corpus (French-language plays), but abstracting away from their huge variability while still providing classes useful for literary research. We fine-tuned transformers-based models to classify against the typology, gradually decreasing the corpus size used for fine tuning, to compare model efficiency with reduced training data. A result comparison speaks in favour of distilled monolingual models for this task, and, unlike earlier research on German, shows no negative effects of model case-sensitivity. The results have practical relevance for computational literary studies, as comparing classification results with complementary stage direction typologies, limiting the amount of manual annotation needed to apply them, would be helpful towards a systematic study of this important textual element.
%U https://aclanthology.org/2024.latechclfl-1.28
%P 278-286
Markdown (Informal)
[Stage Direction Classification in French Theater: Transfer Learning Experiments](https://aclanthology.org/2024.latechclfl-1.28) (Schneider & Ruiz Fabo, LaTeCHCLfL-WS 2024)
ACL