Skeleton-based Coherence Modeling in Narratives
A new neural network tests if tracking a story's 'skeleton' is the key to measuring narrative coherence.
Researchers Nishit Asnani and Rohan Badlani have published a new paper, 'Skeleton-based Coherence Modeling in Narratives,' exploring a fundamental challenge in natural language processing (NLP): how to computationally measure if a story or text flows logically. Their work investigates the concept of a 'skeleton'—a neural network-extracted core structure from a sentence—as a potential metric. They proposed a novel Sentence/Skeleton Similarity Network (SSN) designed to model coherence by analyzing the consistency of these skeletons across pairs of sentences, showing it performs 'much better' than simple baseline techniques like cosine similarity.
However, the study's most significant conclusion is a counterintuitive validation of existing methods. Despite the promise of skeletons, the researchers found that current state-of-the-art coherence modeling techniques, which work with full sentences, actually outperform models based on these sub-sentence skeletons. This indicates the field is on the right track by focusing on holistic sentence analysis rather than breaking text down further. The work has direct applications in tools for detecting incoherent writing and assisting in narrative generation, providing a crucial benchmark for future AI development aimed at creating more logically consistent long-form text.
- Proposed a new Sentence/Skeleton Similarity Network (SSN) that outperforms baseline similarity metrics like cosine similarity.
- Tested the hypothesis that consistency in sentence 'skeletons' is a strong indicator of overall narrative coherence.
- Found that full sentence-level models still outperform skeleton-based approaches, validating current NLP research directions.
Why It Matters
Provides a benchmark for improving AI writing assistants and narrative generation models, ensuring they produce logically coherent long-form text.