DirectQuote: A Dataset for Direct Quotation Extraction and Attribution in News Articles

Yuanchi Zhang, Yang Liu


Abstract
Quotation extraction and attribution are challenging tasks, aiming at determining the spans containing quotations and attributing each quotation to the original speaker. Applying this task to news data is highly related to fact-checking, media monitoring and news tracking. Direct quotations are more traceable and informative, and therefore of great significance among different types of quotations. Therefore, this paper introduces DirectQuote, a corpus containing 19,760 paragraphs and 10,279 direct quotations manually annotated from online news media. To the best of our knowledge, this is the largest and most complete corpus that focuses on direct quotations in news texts. We ensure that each speaker in the annotation can be linked to a specific named entity on Wikidata, benefiting various downstream tasks. In addition, for the first time, we propose several sequence labeling models as baseline methods to extract and attribute quotations simultaneously in an end-to-end manner.
Anthology ID:
2022.lrec-1.752
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
6959–6966
Language:
URL:
https://aclanthology.org/2022.lrec-1.752
DOI:
Bibkey:
Cite (ACL):
Yuanchi Zhang and Yang Liu. 2022. DirectQuote: A Dataset for Direct Quotation Extraction and Attribution in News Articles. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 6959–6966, Marseille, France. European Language Resources Association.
Cite (Informal):
DirectQuote: A Dataset for Direct Quotation Extraction and Attribution in News Articles (Zhang & Liu, LREC 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.lrec-1.752.pdf
Code
 thunlp-mt/directquote