Incorporating Annotator Uncertainty into Representations of Discourse
  Relations
S. Magalí López Cortez Cassandra L. Jacobs
Annotation of discourse relations is a known difficult task, especially for
non-expert annotators. In this paper, we investigate novice annotators'
uncertainty on the annotation of discourse relations on spoken conversational
data. We find that dialogue context (single turn, pair of turns within speaker,
and pair of turns across speakers) is a significant predictor of confidence
scores. We compute distributed representations of discourse relations from
co-occurrence statistics that incorporate information about confidence scores
and dialogue context. We perform a hierarchical clustering analysis using these
representations and show that weighting discourse relation representations with
information about confidence and dialogue context coherently models our
annotators' uncertainty about discourse relation labels.