MLD-EA: Check and Complete Narrative Coherence by Introducing Emotions and Actions
This addresses a gap in NLP for creating more reliable story-generation systems, though it appears incremental as it builds on existing LLM capabilities.
The paper tackles the problem of logical coherence in narrative understanding and story generation by introducing the MLD-EA model, which uses large language models to detect gaps and generate coherent sentences, resulting in enhanced narrative understanding and generation.
Narrative understanding and story generation are critical challenges in natural language processing (NLP), with much of the existing research focused on summarization and question-answering tasks. While previous studies have explored predicting plot endings and generating extended narratives, they often neglect the logical coherence within stories, leaving a significant gap in the field. To address this, we introduce the Missing Logic Detector by Emotion and Action (MLD-EA) model, which leverages large language models (LLMs) to identify narrative gaps and generate coherent sentences that integrate seamlessly with the story's emotional and logical flow. The experimental results demonstrate that the MLD-EA model enhances narrative understanding and story generation, highlighting LLMs' potential as effective logic checkers in story writing with logical coherence and emotional consistency. This work fills a gap in NLP research and advances border goals of creating more sophisticated and reliable story-generation systems.