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

Autor:	Talman, Aarne, Suni, Antti, Celikkanat, Hande, Kakouros, Sofoklis, Tiedemann, Jörg, Vainio, Martti
Rok vydání:	2019
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	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. Comment: NoDaLiDa 2019 camera ready
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1908.02262 Zobrazit plný text záznamu View this record from Arxiv