  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.

Computation and Language;Machine Learning