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T1  - Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations 
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UR  - http://hdl.handle.net/10138/311873 
T3  - Linköping Electronic Conference Proceedings 
A1  - Talman, Aarne; Suni, Antti; Celikkanat, Hande; Kakouros, Sofoklis; Tiedemann, Jörg; Vainio, Martti 
A2  - Hartmann, Mareike; Plank, Barbara 
PB  - Linköping University Electronic Press 
Y1  - 2019 
LA  - eng 
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 conte... 
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KW  - 113 Computer and information sciences; Natural language processing; 6121 Languages 
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