基于指令微调大语言模型与数据增强的跨度级情感-原因-类别三元组抽取
Span-level Emotion-Cause-Category Triplet Extraction with Instruction Tuning LLMs and Data Augmentation
AI总结：
针对跨度级情感-原因-类别三元组抽取的挑战，本研究提出基于指令微调和数据增强的框架，无需特定任务架构，解决数据稀缺问题，实验表明其指标较基线提升至少12.8%，推动情感原因分析发展。
摘要：
跨度级情感-原因-类别三元组抽取是情感原因分析领域中一项新颖且复杂的挑战。该任务需要识别文本中的情感跨度、原因跨度及其关联的情感类别，以形成结构化三元组。尽管先前研究主要集中在子句级情感-原因对抽取和跨度级情感-原因检测，但这些方法常面临冗余信息检索以及难以准确确定情感类别的挑战，尤其是当情感以隐式或模糊方式表达时。为克服这些挑战，本研究探索了一种细粒度的跨度级情感-原因-类别三元组抽取方法，并引入了一个创新框架，该框架利用基于大语言模型的指令微调和数据增强技术。所提出的方法采用特定任务的三元组抽取指令，并利用低秩适应（LoRA）对大语言模型进行微调，从而无需复杂的特定任务架构。此外，本研究开发了一种基于提示的数据增强策略，通过引导大语言模型生成高质量的合成训练数据来解决数据稀缺问题。大量实验评估表明，所提出的方法显著优于现有基线方法，在跨度级情感-原因-类别三元组抽取指标上实现了至少12.8%的提升。结果证明了该方法的有效性和鲁棒性，为推进情感原因分析研究提供了有前景的途径。源代码可在https://github.com/zxgnlp/InstruDa-LLM获取。
英文摘要：
Span-level emotion-cause-category triplet extraction represents a novel and complex challenge within emotion cause analysis. This task involves identifying emotion spans, cause spans, and their associated emotion categories within the text to form structured triplets. While prior research has predominantly concentrated on clause-level emotion-cause pair extraction and span-level emotion-cause detection, these methods often confront challenges originating from redundant information retrieval and difficulty in accurately determining emotion categories, particularly when emotions are expressed implicitly or ambiguously. To overcome these challenges, this study explores a fine-grained approach to span-level emotion-cause-category triplet extraction and introduces an innovative framework that leverages instruction tuning and data augmentation techniques based on large language models. The proposed method employs task-specific triplet extraction instructions and utilizes low-rank adaptation to fine-tune large language models, eliminating the necessity for intricate task-specific architectures. Furthermore, a prompt-based data augmentation strategy is developed to address data scarcity by guiding large language models in generating high-quality synthetic training data. Extensive experimental evaluations demonstrate that the proposed approach significantly outperforms existing baseline methods, achieving at least a 12.8% improvement in span-level emotion-cause-category triplet extraction metrics. The results demonstrate the method's effectiveness and robustness, offering a promising avenue for advancing research in emotion cause analysis. The source code is available at https://github.com/zxgnlp/InstruDa-LLM.
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。
