Our experiments investigate the relevance of our connective-based representations for implicit discourse relation identification, recast here as multiclass classification problem.
we aim at evaluating the usefulness of having a word representation linked to the task, compared to using generic  word representations (either one-hot, cluster-based or distributed), and whether they encode all the information relevant to the task, thus comparing systems with or without additional hand-crafted features.