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：叙述性文本中引语说话人的识别是文学分析中的一项重要任务，其挑战性场景包括对未见说话人的跨域推理，以及周围语境未提及说话人的非显式情况。本文提出一种简洁高效的SIG方法，这是一种基于生成的方法，通过设计提示模板将任务和引语输入进行语言化表达，并能够轻松整合其他辅助任务以进一步提升说话人识别性能。预测结果可直接由模型生成，也可通过各候选说话人最高生成概率确定。基于该方法设计，SIG支持跨域评估，并实现可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上进行跨域评估与域内评估，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，尤其在难以处理的非显式场景中性能提升高达17%。在另一数据集WP上的附加实验进一步验证了SIG的有效性。