We hypothesize that this sentence-based approach, which exploits a training set in which each sentence in a post is labeled with its reason, would achieve better performance than a multilabel text classification approach to post-level RC, which learns to determine the subset of reasons a post contains directly from a training set in which each post is labeled with the corresponding set of reasons.
we hypothesize that we could achieve better results for post-level RC by learning from sentence-level than from post-level reason annotations, as sentence-level reason annotations can enable a learning algorithm to accurately attribute an annotated reason to a particular portion of a post.