2024/08/18 by Yuchen Yan, Hanjie Zhao, Yan, Yuchen +9
Computer Science · Psychology · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Innovative Teaching and Learning Methods #Intelligent Tutoring Systems and Adaptive Learning #Reading and Literacy Development
paper · pdf · doi:10.48550/arxiv.2408.09452
openalex publication_date 2024/08/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.