@InProceedings{laali-kosseim:2017:RANLP,
  author    = {Laali, Majid  and  Kosseim, Leila},
  title     = {Improving Discourse Relation Projection to Build Discourse Annotated Corpora},
  booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {407--416},
  abstract  = {The naive approach to annotation projection is not effective to project
	discourse annotations from one language to another because implicit relations
	are often changed to explicit ones and vice-versa in the translation. In this
	paper, we propose a novel approach based on the intersection between
	statistical word-alignment models to identify unsupported discourse
	annotations. This approach identified 65% of the unsupported annotations in the
	English-French parallel sentences from Europarl. By filtering out these
	unsupported annotations, we induced the first PDTB-style discourse annotated
	corpus for French from Europarl. We then used this corpus to train a classifier
	to identify the discourse-usage of French discourse connectives and show a 15%
	improvement of F1-score compared to the classifier trained on the non-filtered
	annotations.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_054}
}