@inproceedings{vishnubhotla-etal-2023-improving,
title = "Improving Automatic Quotation Attribution in Literary Novels",
author = "Vishnubhotla, Krishnapriya and
Rudzicz, Frank and
Hirst, Graeme and
Hammond, Adam",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.64",
doi = "10.18653/v1/2023.acl-short.64",
pages = "737--746",
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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<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.</abstract>
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%0 Conference Proceedings
%T Improving Automatic Quotation Attribution in Literary Novels
%A Vishnubhotla, Krishnapriya
%A Rudzicz, Frank
%A Hirst, Graeme
%A Hammond, Adam
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F vishnubhotla-etal-2023-improving
%X 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.
%R 10.18653/v1/2023.acl-short.64
%U https://aclanthology.org/2023.acl-short.64
%U https://doi.org/10.18653/v1/2023.acl-short.64
%P 737-746
Markdown (Informal)
[Improving Automatic Quotation Attribution in Literary Novels](https://aclanthology.org/2023.acl-short.64) (Vishnubhotla et al., ACL 2023)
ACL
- Krishnapriya Vishnubhotla, Frank Rudzicz, Graeme Hirst, and Adam Hammond. 2023. Improving Automatic Quotation Attribution in Literary Novels. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 737–746, Toronto, Canada. Association for Computational Linguistics.