Cause and Effect in Governmental Reports: Two Data Sets for Causality Detection in Swedish

Luise Dürlich, Sebastian Reimann, Gustav Finnveden, Joakim Nivre, Sara Stymne


Abstract
Causality detection is the task of extracting information about causal relations from text. It is an important task for different types of document analysis, including political impact assessment. We present two new data sets for causality detection in Swedish. The first data set is annotated with binary relevance judgments, indicating whether a sentence contains causality information or not. In the second data set, sentence pairs are ranked for relevance with respect to a causality query, containing a specific hypothesized cause and/or effect. Both data sets are carefully curated and mainly intended for use as test data. We describe the data sets and their annotation, including detailed annotation guidelines. In addition, we present pilot experiments on cross-lingual zero-shot and few-shot causality detection, using training data from English and German.
Anthology ID:
2022.politicalnlp-1.7
Volume:
Proceedings of the LREC 2022 workshop on Natural Language Processing for Political Sciences
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Haithem Afli, Mehwish Alam, Houda Bouamor, Cristina Blasi Casagran, Colleen Boland, Sahar Ghannay
Venue:
PoliticalNLP
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
46–55
Language:
URL:
https://aclanthology.org/2022.politicalnlp-1.7
DOI:
Bibkey:
Cite (ACL):
Luise Dürlich, Sebastian Reimann, Gustav Finnveden, Joakim Nivre, and Sara Stymne. 2022. Cause and Effect in Governmental Reports: Two Data Sets for Causality Detection in Swedish. In Proceedings of the LREC 2022 workshop on Natural Language Processing for Political Sciences, pages 46–55, Marseille, France. European Language Resources Association.
Cite (Informal):
Cause and Effect in Governmental Reports: Two Data Sets for Causality Detection in Swedish (Dürlich et al., PoliticalNLP 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.politicalnlp-1.7.pdf
Code
 uppsalanlp/sou-corpus +  additional community code