By augmenting a labeled dataset with unlabeled data, we can use a bootstrapping framework to improve predictive accuracy, and reduce the need for labeled data-which could make it much easier to port discourse parsing algorithms to new domains.
a fully unsupervised parser may not be desirable because in many applications specific discourse relations must be identified, which would be difficult to achieve without the use of labeled examples.