Article | NEAL Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa), September 30 - October 2, Turku, Finland | Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations Linköping University Electronic Press Conference Proceedings
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Title:
Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations
Author:
Aarne Talman: Department of Digital Humanities, University of Helsinki, Finland / Basement AI, Finland Antti Suni: Department of Digital Humanities, University of Helsinki, Finland Hande Celikkanat: Department of Digital Humanities, University of Helsinki, Finland Sofoklis Kakouros: Department of Digital Humanities, University of Helsinki, Finland Jörg Tiedemann: Department of Digital Humanities, University of Helsinki, Finland Martti Vainio: Department of Digital Humanities, University of Helsinki, Finland
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Full text (pdf)
Year:
2019
Conference:
NEAL Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa), September 30 - October 2, Turku, Finland
Issue:
167
Article no.:
029
Pages:
281--290
No. of pages:
9
Publication type:
Abstract and Fulltext
Published:
2019-10-02
ISBN:
978-91-7929-995-8
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Series:
NEALT Proceedings Series
Publisher:
Linköping University Electronic Press, Linköpings universitet


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In this paper we introduce a new natural language processing dataset and benchmark for predicting prosodic prominence from written text. To our knowledge this will be the largest publicly available dataset with prosodic labels. We describe the dataset construction and the resulting benchmark dataset in detail and train a number of different models ranging from feature-based classifiers to neural network systems for the prediction of discretized prosodic prominence. We show that pre-trained contextualized word representations from BERT outperform the other models even with less than 10% of the training data. Finally we discuss the dataset in light of the results and point to future research and plans for further improving both the dataset and methods of predicting prosodic prominence from text. The dataset and the code for the models will be made publicly available.

Keywords: prosody prediction prosodic prominence sequence labeling contextualized word representations

NEAL Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa), September 30 - October 2, Turku, Finland

Author:
Aarne Talman, Antti Suni, Hande Celikkanat, Sofoklis Kakouros, Jörg Tiedemann, Martti Vainio
Title:
Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations
References:
No references available

NEAL Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa), September 30 - October 2, Turku, Finland

Author:
Aarne Talman, Antti Suni, Hande Celikkanat, Sofoklis Kakouros, Jörg Tiedemann, Martti Vainio
Title:
Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations
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Last updated: 2019-11-06