用情绪和动作补全故事逻辑漏洞，让生成内容更连贯
MLD-EA: Check and Complete Narrative Coherence by Introducing Emotions and Actions
- 通过情绪与动作识别故事中的逻辑断点
- 提升故事连贯性，使生成内容更符合情感与逻辑流
- 适合需要高质量叙事的AI写作、游戏剧情生成场景
叙事理解与生成是自然语言处理中的关键挑战，现有研究多集中于摘要和问答任务。尽管已有工作尝试预测情节结尾并生成扩展叙事，但常忽略故事内部的逻辑连贯性，留下显著空白。为此，我们提出基于情绪与动作的缺失逻辑检测器（MLD-EA）模型，利用大语言模型（LLMs）识别叙事断层，并生成与故事情感及逻辑流无缝衔接的句子。实验表明，MLD-EA模型显著提升了叙事理解与生成能力，凸显了大语言模型在保障情节连贯性和情感一致性方面的逻辑校验潜力。本工作填补了NLP研究空白，推动了更复杂可靠叙事生成系统的发展。
原文摘要 · Abstract (English)
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.
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