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arXiv 2604.02451cs.CLcs.AI
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基于骨架的叙述连贯性建模

Skeleton-based Coherence Modeling in Narratives

Nishit Asnani, Rohan Badlani

AI总结:

本文研究骨架在后续句子中的一致性作为连贯性度量的可行性,提出SSN模型优于基线方法,表明当前连贯性建模技术通过处理句子而非子部分是正确的方向。

摘要:

文本连贯性建模已激发NLP研究者长时间的兴趣。它在检测不连贯结构和帮助作者修复它们方面有应用。最近有工作利用神经网络从一个句子中提取骨架,然后用该骨架生成下一句以实现连贯的叙述故事生成。在本项目中,我们旨在研究后续句子中骨架的一致性是否是衡量给定文本连贯性的良好指标。我们提出了一种新的句子/骨架相似度网络(SSN)用于建模句子对之间的连贯性,并证明该网络比基线相似性技术如余弦相似度和欧几里得距离表现更好。尽管骨架似乎是建模连贯性的有希望的候选者,但我们的结果表明,句子级模型在评估文本连贯性时优于骨架模型,这表明当前最先进的连贯性建模技术通过处理句子而非其子部分是正确的方向。

英文摘要:

Modeling coherence in text has been a task that has excited NLP researchers since a long time. It has applications in detecting incoherent structures and helping the author fix them. There has been recent work in using neural networks to extract a skeleton from one sentence, and then use that skeleton to generate the next sentence for coherent narrative story generation. In this project, we aim to study if the consistency of skeletons across subsequent sentences is a good metric to characterize the coherence of a given body of text. We propose a new Sentence/Skeleton Similarity Network (SSN) for modeling coherence across pairs of sentences, and show that this network performs much better than baseline similarity techniques like cosine similarity and Euclidean distance. Although skeletons appear to be promising candidates for modeling coherence, our results show that sentence-level models outperform those on skeletons for evaluating textual coherence, thus indicating that the current state-of-the-art coherence modeling techniques are going in the right direction by dealing with sentences rather than their sub-parts.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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