CausalQA: A Benchmark for Causal Question Answering

Alexander Bondarenko, Magdalena Wolska, Stefan Heindorf, Lukas Blübaum, Axel-Cyrille Ngonga Ngomo, Benno Stein, Pavel Braslavski, Matthias Hagen, Martin Potthast


Abstract
At least 5% of questions submitted to search engines ask about cause-effect relationships in some way. To support the development of tailored approaches that can answer such questions, we construct Webis-CausalQA-22, a benchmark corpus of 1.1 million causal questions with answers. We distinguish different types of causal questions using a novel typology derived from a data-driven, manual analysis of questions from ten large question answering (QA) datasets. Using high-precision lexical rules, we extract causal questions of each type from these datasets to create our corpus. As an initial baseline, the state-of-the-art QA model UnifiedQA achieves a ROUGE-L F1 score of 0.48 on our new benchmark.
Anthology ID:
2022.coling-1.291
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
3296–3308
Language:
URL:
https://aclanthology.org/2022.coling-1.291
DOI:
Bibkey:
Cite (ACL):
Alexander Bondarenko, Magdalena Wolska, Stefan Heindorf, Lukas Blübaum, Axel-Cyrille Ngonga Ngomo, Benno Stein, Pavel Braslavski, Matthias Hagen, and Martin Potthast. 2022. CausalQA: A Benchmark for Causal Question Answering. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3296–3308, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
CausalQA: A Benchmark for Causal Question Answering (Bondarenko et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.291.pdf
Code
 webis-de/coling-22
Data
CommonsenseQAELI5GooAQHotpotQAMS MARCONatural QuestionsNewsQAPAQSQuADSearchQATriviaQA