Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations

Aarne Talman; Antti Suni; Hande Celikkanat; Sofoklis Kakouros; Jörg Tiedemann; Martti Vainio

Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations

Aarne Talman, Antti Suni, Hande Celikkanat, Sofoklis Kakouros, Jörg Tiedemann, Martti Vainio

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Abstract

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 are publicly available.

Original language	English
Title of host publication	22nd Nordic Conference on Computational Linguistics (NoDaLiDa) : Proceedings of the Conference
Editors	Mareike Hartmann, Barbara Plank
Number of pages	10
Place of Publication	Linköping
Publisher	Linköping University Electronic Press
Publication date	30 Sept 2019
Pages	281–290
ISBN (Electronic)	978-91-7929-995-8
Publication status	Published - 30 Sept 2019
MoE publication type	A4 Article in conference proceedings
Event	Nordic Conference on Computational Linguistics - Turku, Finland Duration: 30 Sept 2019 → 2 Oct 2019 Conference number: 22 https://nodalida2019.org/

Publication series

Name	Linköping Electronic Conference Proceedings
Publisher	Linköping University Electronic Press
Number	167
ISSN (Print)	1650-3686
ISSN (Electronic)	1650-3740

Name	NEALT Proceedings Series
Number	42

Fields of Science

113 Computer and information sciences
Natural language processing
6121 Languages

Access to Document

W19-6129Final published version, 572 KBLicence: CC BY

Cite this

Talman, A., Suni, A., Celikkanat, H., Kakouros, S., Tiedemann, J., & Vainio, M. (2019). Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations. In M. Hartmann, & B. Plank (Eds.), 22nd Nordic Conference on Computational Linguistics (NoDaLiDa): Proceedings of the Conference (pp. 281–290). (Linköping Electronic Conference Proceedings; No. 167), (NEALT Proceedings Series; No. 42). Linköping University Electronic Press.

Talman, Aarne ; Suni, Antti ; Celikkanat, Hande et al. / Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations. 22nd Nordic Conference on Computational Linguistics (NoDaLiDa): Proceedings of the Conference. editor / Mareike Hartmann ; Barbara Plank. Linköping : Linköping University Electronic Press, 2019. pp. 281–290 (Linköping Electronic Conference Proceedings; 167). (NEALT Proceedings Series; 42).

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title = "Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations",

abstract = "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 are publicly available.",

keywords = "113 Computer and information sciences, Natural language processing, 6121 Languages",

author = "Aarne Talman and Antti Suni and Hande Celikkanat and Sofoklis Kakouros and J{\"o}rg Tiedemann and Martti Vainio",

year = "2019",

month = sep,

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series = "Link{\"o}ping Electronic Conference Proceedings",

publisher = "Link{\"o}ping University Electronic Press",

number = "167",

pages = "281–290",

editor = "Mareike Hartmann and Plank, {Barbara }",

booktitle = "22nd Nordic Conference on Computational Linguistics (NoDaLiDa)",

address = "Sweden",

note = "Nordic Conference on Computational Linguistics, NoDaLiDa ; Conference date: 30-09-2019 Through 02-10-2019",

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Talman, A , Suni, A , Celikkanat, H , Kakouros, S , Tiedemann, J & Vainio, M 2019, Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations. in M Hartmann & B Plank (eds), 22nd Nordic Conference on Computational Linguistics (NoDaLiDa): Proceedings of the Conference. Linköping Electronic Conference Proceedings, no. 167, NEALT Proceedings Series, no. 42, Linköping University Electronic Press, Linköping, pp. 281–290, Nordic Conference on Computational Linguistics, Turku, Finland, 30/09/2019.

Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations. / Talman, Aarne ; Suni, Antti ; Celikkanat, Hande et al.
22nd Nordic Conference on Computational Linguistics (NoDaLiDa): Proceedings of the Conference. ed. / Mareike Hartmann; Barbara Plank. Linköping: Linköping University Electronic Press, 2019. p. 281–290 (Linköping Electronic Conference Proceedings; No. 167), (NEALT Proceedings Series; No. 42).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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AU - Kakouros, Sofoklis

AU - Tiedemann, Jörg

AU - Vainio, Martti

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N2 - 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 are publicly available.

AB - 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 are publicly available.

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KW - Natural language processing

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Talman A , Suni A , Celikkanat H , Kakouros S , Tiedemann J , Vainio M. Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations. In Hartmann M, Plank B, editors, 22nd Nordic Conference on Computational Linguistics (NoDaLiDa): Proceedings of the Conference. Linköping: Linköping University Electronic Press. 2019. p. 281–290. (Linköping Electronic Conference Proceedings; 167). (NEALT Proceedings Series; 42).