  The naive approach to annotation projection is not effective to project
discourse annotations from one language to another because implicit discourse
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.
