Motivated by the increasing popularity of intelligent editing assistant, we introduce and investigate the task of narrative incoherence detection  : Given a (corrupted) long-form narrative, decide whether there exists some semantic discrepancy in the narrative flow. Specifically, we focus on the missing sentence and incoherent sentence detection. Despite its simple setup, this task is challenging as the model needs to understand and analyze a multi-sentence narrative text, and make decisions at the sentence level. As an initial step towards this task, we implement several baselines either directly analyzing the raw text (token-level) or analyzing learned sentence representations (sentence-level). We observe that while token-level modeling enjoys greater expressive power and hence better performance , sentence-level modeling  possesses an advantage in efficiency and flexibility. With pre-training on large-scale data and cycle-consistent sentence embedding, our extended sentence-level model can achieve comparable detection accuracy to the token-level model. As a by-product, such a strategy enables simultaneous incoherence detection and infilling/modification suggestions .
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 (token-level) or analyzing learned sentence representations (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 .
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Motivated by the increasing popularity of intelligent editing assistant, we introduce and investigate the
We propose the
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as a new arena for inter-sentential semantic understanding
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(corrupted) long-form
multi-sentence
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exists some semantic discrepancy
exist any semantic discrepancies
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incoherent
discordant
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text, and make decisions
, and predict incoherence
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enjoys greater expressive power and hence better performance
has better performance when the input contains fewer sentences
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performs better on longer narratives and
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With pre-training
Pre-training
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cycle-consistent sentence embedding, our extended sentence-level model can achieve comparable detection accuracy to the token-level model. As a by-product, such a strategy enables simultaneous incoherence detection and infilling/modification suggestions
auxiliary sentence prediction training objective further boost the detection performance of the sentence-level model