Coherence Modeling Improves Implicit Discourse Relation Recognition
Noriki Nishida, Hideki Nakayama · 2018
The research described in this paper examines how to learn linguistic knowledge associated with discourse relations from unlabeled corpora.We introduce an unsupervised learning method on text coherence that could produce numerical representations that improve implicit discourse relation recognition in a semi-supervised manner.We also empirically examine two variants of coherence modeling: orderoriented and topic-oriented negative sampling, showing that, of the two, topicoriented negative sampling tends to be more effective.