基于提示学习的小说引语说话者与受话者识别
Identifying Speakers and Addressees of Quotations in Novels with Prompt Learning
AI总结：
针对小说引语受话者识别研究不足及数据集稀缺问题，标注首个含多要素的中文引语语料库，提出基于微调预训练模型的提示学习方法，在中英文数据集上效果优于零样本、少样本大语言模型方法。
摘要：
文学作品尤其是小说中的引语对塑造人物、反映人物关系和推动情节发展具有重要作用。当前小说引语抽取研究主要聚焦于引语归属，即识别引语的说话者。然而，引语的受话者对于构建说话者与受话者之间的关系同样重要。为解决数据集稀缺问题，我们标注了首个包含说话者、受话者、说话方式和语言线索等要素的中文引语语料库。我们在微调预训练模型的基础上，提出了基于提示学习的说话者和受话者识别方法。在中英文数据集上的实验均表明了所提方法的有效性，其性能优于基于零样本和少样本大语言模型的方法。
英文摘要：
Quotations in literary works, especially novels, are important to create characters, reflect character relationships, and drive plot development. Current research on quotation extraction in novels primarily focuses on quotation attribution, i.e., identifying the speaker of the quotation. However, the addressee of the quotation is also important to construct the relationship between the speaker and the addressee. To tackle the problem of dataset scarcity, we annotate the first Chinese quotation corpus with elements including speaker, addressee, speaking mode and linguistic cue. We propose prompt learning-based methods for speaker and addressee identification based on fine-tuned pre-trained models. Experiments on both Chinese and English datasets show the effectiveness of the proposed methods, which outperform methods based on zero-shot and few-shot large language models.
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。
