Dual-Glob applies supervised contrastive learning to classify fine-grained pitch accent patterns from F0 contours in Seoul Korean, achieving 77.75% accuracy and 51.54% F1 on a new dataset of 10,093 manually annotated accentual phrases.
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Presents multitask variational sequential labelers using latent variables for word prediction and label discrimination that outperform baselines on 8 datasets and benefit from unlabeled data.
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Variational Sequential Labelers for Semi-Supervised Learning
Presents multitask variational sequential labelers using latent variables for word prediction and label discrimination that outperform baselines on 8 datasets and benefit from unlabeled data.