Identifying speakers of quotations in narratives is an important task in literary analysis, with challenging scenarios including the out-of-domain inference for unseen speakers, and non-explicit cases where there are no speaker mentions in surrounding context. In this work, we propose a simple and effective approach SIG, a generation-based method that verbalizes the task and quotation input based on designed prompt templates, which also enables easy integration of other auxiliary tasks that further bolster the speaker identification performance. The prediction can either come from direct generation by the model, or be determined by the highest generation probability of each speaker candidate. Based on our approach design, SIG supports out-of-domain evaluation, and achieves open-world classification paradigm that is able to accept any forms of candidate input. We perform both cross-domain evaluation and in-domain evaluation on PDNC, the largest dataset of this task, where empirical results suggest that SIG outperforms previous baselines of complicated designs, as well as the zero-shot ChatGPT, especially excelling at those hard non-explicit scenarios by up to 17% improvement. Additional experiments on another dataset WP further corroborate the efficacy of SIG.

翻译：叙事文本中引语的说话人识别是文学分析中的重要任务，其难点包括未见说话人的跨域推理，以及上下文未提及说话人的非显式场景。本文提出一种简单有效的方法SIG——一种基于生成的方法，通过设计提示模板对任务和引语输入进行言语化，该方法还能便捷集成其他辅助任务以进一步提升说话人识别性能。预测结果既可直接由模型生成获得，也可通过各候选说话人的最高生成概率确定。基于该方法设计，SIG支持跨域评估，并实现能够接受任意形式候选输入的开世界分类范式。我们在该任务最大数据集PDNC上进行了跨域评估和域内评估，实验结果表明SIG优于先前设计复杂的基线模型及零样本ChatGPT，尤其在处理困难的非显式场景时性能提升高达17%。基于另一数据集WP的补充实验进一步验证了SIG的有效性。