  We propose the task of narrative incoherence detection as a new arena for
inter-sentential semantic understanding: Given a multi-sentence narrative,
decide whether there exist any semantic discrepancies in the narrative flow.
Specifically, we focus on the missing sentence and discordant sentence
detection. Despite its simple setup, this task is challenging as the model
needs to understand and analyze a multi-sentence narrative, and predict
incoherence at the sentence level. As an initial step towards this task, we
implement several baselines either directly analyzing the raw text
(\textit{token-level}) or analyzing learned sentence representations
(\textit{sentence-level}). We observe that while token-level modeling has
better performance when the input contains fewer sentences, sentence-level
modeling performs better on longer narratives and possesses an advantage in
efficiency and flexibility. Pre-training on large-scale data and auxiliary
sentence prediction training objective further boost the detection performance
of the sentence-level model.
