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.

介绍了一个新的自然语言处理数据集和用于预测从书面文本中的韵律突出的基准测试。使用预训练的上下文化单词表示方法 BERT，比其他模型快 10% 的预测度与作为离散化的韵律突出显示。