Fast and Accurate Quotation Attribution in Literary Texts
Attributing quotations to their speakers in literary texts remains an open challenge. Standard methods, which independently predict a speaker mention for each quotation, are efficient but still limited in accuracy. In contrast, large language model (LLM) approaches achieve strong performance, but their computational cost limits their use in large-scale literary analysis. We propose an encoder-based efficient formulation that resolves multiple quotation attributions within a shared, large context window. Using our new formulation, \textit{joint scoring}, we report state-of-the-art (SOTA) performance on the Project Dialogism Novel Corpus (PDNC), comprising more than 35,000 manually annotate...
| Authors | Gaspard Michel, Hugo Attali, Elena V. Epure |
|---|---|
| Primary category | cs.CL |
| Published | 3 August 2026 |
| Last updated at source | 3 August 2026 |
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