Discourse parsing is the process of discovering the latent relational structure of a long form piece of text and remains a significant open challenge. One of the most difficult tasks in discourse parsing is the classification of implicit discourse relations. Most state-of-the-art systems do not leverage the great volume of unlabeled text available on the web-they rely instead on human annotated training data. By incorporating a mixture of labeled and unlabeled data, we are able to improve relation classification accuracy, reduce the need for annotated data, while still retaining the capacity to use labeled data to ensure that specific desired relations are learned. We achieve this using a latent variable model that is trained in a reduced dimensionality subspace using spectral methods. Our approach achieves an F 1 score of 0.485 on the implicit relation labeling task for the Penn Discourse Treebank.
REF incorporate a mixture of labeled and unlabeled data to reduce the need for annotated data.
Spectral Semi-Supervised Discourse Relation Classification
ACL
Computer Science