Improving Automatic Quotation Attribution in Literary Novels

• URL: http://arxiv.org/abs/2307.03734v1
• Date: Fri, 7 Jul 2023 17:37:01 GMT
• Title: Improving Automatic Quotation Attribution in Literary Novels
• Authors: Krishnapriya Vishnubhotla, Frank Rudzicz, Graeme Hirst, Adam Hammond
• Abstract summary: Current models for quotation attribution in literary novels assume varying levels of available information in their training and test data. 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. 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.
• Score: 21.164701493247794
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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