@InProceedings{roesiger-EtAl:2017:Speech-Centric,
  author    = {Roesiger, Ina  and  Stehwien, Sabrina  and  Riester, Arndt  and  Vu, Ngoc Thang},
  title     = {Improving coreference resolution with automatically predicted prosodic information},
  booktitle = {Proceedings of the Workshop on Speech-Centric Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {78--83},
  abstract  = {Adding manually annotated prosodic information, specifically pitch accents and
	phrasing, to the typical text-based feature set for coreference resolution has
	previously been shown to have a positive effect on German data. Practical
	applications on spoken language, however, would rely on automatically predicted
	prosodic information. In this paper we predict pitch accents (and phrase
	boundaries) using a convolutional neural network (CNN) model from acoustic
	features extracted from the speech signal. After an assessment of the quality
	of these automatic prosodic annotations, we show that they also significantly
	improve coreference resolution.},
  url       = {http://www.aclweb.org/anthology/W17-4610}
}