Improving Automatic Quotation Attribution in Literary Novels
Krishnapriya Vishnubhotla Frank Rudzicz Graeme Hirst Adam Hammond
Current models for quotation attribution in literary novels assume varying
levels of available information in their training and test data, which poses a
challenge for in-the-wild inference. Here, we approach quotation attribution as
a set of four interconnected sub-tasks: character identification, coreference
resolution, quotation identification, and speaker attribution. We benchmark
state-of-the-art models on each of these sub-tasks independently, using a large
dataset of annotated coreferences and quotations in literary novels (the
Project Dialogism Novel Corpus). We also train and evaluate models for the
speaker attribution task in particular, showing that a simple sequential
prediction model achieves accuracy scores on par with state-of-the-art models.