Narrative Incoherence Detection

Abstract

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.

References in corpus (6)

• Language Models are Few-Shot Learners
• MASS: Masked Sequence to Sequence Pre-training for Language Generation
• MaskGAN: Better Text Generation via Filling in the______
• A Simple Method for Commonsense Reasoning
• Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization
• XL-Editor: Post-editing Sentences with XLNet