Papers › Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations
Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations
Aarne Talman, Antti Suni, Hande Celikkanat, Sofoklis Kakouros, Jörg Tiedemann, Martti Vainio
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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Code
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Datasets
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Prosody Prediction | Helsinki Prosody Corpus | BERT | Accuracy | 83.2 | #1 of 4 | Archive leaderboard | report |
| Prosody Prediction | Helsinki Prosody Corpus | BiLSTM | Accuracy | 82.1 | #2 of 4 | Archive leaderboard | report |
| Prosody Prediction | Helsinki Prosody Corpus | CRF (MarMoT) | Accuracy | 81.8 | #3 of 4 | Archive leaderboard | report |
| Prosody Prediction | Helsinki Prosody Corpus | SVN (Minitagger) | Accuracy | 80.8 | #4 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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