ELSA：面向情感智能语言生成的风格对齐数据集
ELSA: A Style Aligned Dataset for Emotionally Intelligent Language Generation
AI总结：
本文提出ELSA数据集，通过细粒度情感分类与多种语境风格重写原始句子，并利用大语言模型生成情感细微变化文本，以支持情感条件风格自适应文本生成研究。
摘要：
情感感知语言处理的进展日益塑造着从对话式人工智能、情感计算到计算心理学和创意内容生成等一系列重要NLP应用。现有情感数据集要么缺乏情感粒度，要么未能捕捉必要的风格多样性，限制了有效的情感条件文本生成系统的发展。为弥合粒度与风格多样性之间的这一关键差距，本文引入了一个新颖的系统化构建数据集，名为ELSA（情感与语言风格对齐数据集），该数据集利用了从dair-ai emotion dataset和GoEmotions分类法等现有来源改编而来的细粒度情感分类体系。该数据集包含原始句子在对话式、正式、诗意和叙事等不同语境风格下重新生成的多个情感细微变化版本，并使用先进的大语言模型（LLMs）生成。使用困惑度、嵌入方差、可读性、词汇多样性和语义连贯性度量等指标进行的严格计算评估验证了该数据集的情感真实性、语言流畅性和文本多样性。全面的度量分析证实了其支持对情感条件风格自适应文本生成进行更深入探索的潜力。通过实现精确调优的情感细微语言建模，我们的数据集为利用LLMs进行细粒度情感控制、提示驱动解释、可解释性以及风格自适应表达性语言生成等研究创造了肥沃的土壤。
英文摘要：
Advancements in emotion aware language processing increasingly shape vital NLP applications ranging from conversational AI and affective computing to computational psychology and creative content generation. Existing emotion datasets either lack emotional granularity or fail to capture necessary stylistic diversity, limiting the advancement of effective emotion conditioned text generation systems. Seeking to bridge this crucial gap between granularity and style diversity, this paper introduces a novel systematically constructed dataset named ELSA Emotion and Language Style Alignment Dataset leveraging fine grained emotion taxonomies adapted from existing sources such as dair ai emotion dataset and GoEmotions taxonomy. This dataset comprises multiple emotionally nuanced variations of original sentences regenerated across distinct contextual styles such as conversational, formal, poetic, and narrative, using advanced Large Language Models LLMs. Rigorous computational evaluation using metrics such as perplexity, embedding variance, readability, lexical diversity, and semantic coherence measures validates the datasets emotional authenticity, linguistic fluency, and textual diversity. Comprehensive metric analyses affirm its potential to support deeper explorations into emotion conditioned style adaptive text generation. By enabling precision tuned emotionally nuanced language modeling, our dataset creates fertile ground for research on fine grained emotional control, prompt driven explanation, interpretability, and style adaptive expressive language generation with LLMs.
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。
