CausalQA: A Benchmark for Causal Question Answering

A. Bondarenko, M. Wolska, S. Heindorf, L. Blübaum, A.-C. Ngonga Ngomo, B. Stein, P. Braslavski, M. Hagen, M. Potthast, in: Proceedings of the 29th International Conference on Computational Linguistics, International Committee on Computational Linguistics, Gyeongju, Republic of Korea, 2022, pp. 3296–3308.

Conference Paper | English
Author
Bondarenko, Alexander; Wolska, Magdalena; Heindorf, StefanLibreCat ; Blübaum, Lukas; Ngonga Ngomo, Axel-CyrilleLibreCat; Stein, Benno; Braslavski, Pavel; Hagen, Matthias; Potthast, Martin
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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.
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Proceedings Title
Proceedings of the 29th International Conference on Computational Linguistics
Page
3296–3308
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Bondarenko A, Wolska M, Heindorf S, et al. CausalQA: A Benchmark for Causal Question Answering. In: Proceedings of the 29th International Conference on Computational Linguistics. International Committee on Computational Linguistics; 2022:3296–3308.
Bondarenko, A., Wolska, M., Heindorf, S., Blübaum, L., Ngonga Ngomo, A.-C., Stein, B., Braslavski, P., Hagen, M., & Potthast, M. (2022). CausalQA: A Benchmark for Causal Question Answering. Proceedings of the 29th International Conference on Computational Linguistics, 3296–3308.
@inproceedings{Bondarenko_Wolska_Heindorf_Blübaum_Ngonga Ngomo_Stein_Braslavski_Hagen_Potthast_2022, place={Gyeongju, Republic of Korea}, title={CausalQA: A Benchmark for Causal Question Answering}, booktitle={Proceedings of the 29th International Conference on Computational Linguistics}, publisher={International Committee on Computational Linguistics}, author={Bondarenko, Alexander and Wolska, Magdalena and Heindorf, Stefan and Blübaum, Lukas and Ngonga Ngomo, Axel-Cyrille and Stein, Benno and Braslavski, Pavel and Hagen, Matthias and Potthast, Martin}, year={2022}, pages={3296–3308} }
Bondarenko, Alexander, Magdalena Wolska, Stefan Heindorf, Lukas Blübaum, Axel-Cyrille Ngonga Ngomo, Benno Stein, Pavel Braslavski, Matthias Hagen, and Martin Potthast. “CausalQA: A Benchmark for Causal Question Answering.” In Proceedings of the 29th International Conference on Computational Linguistics, 3296–3308. Gyeongju, Republic of Korea: International Committee on Computational Linguistics, 2022.
A. Bondarenko et al., “CausalQA: A Benchmark for Causal Question Answering,” in Proceedings of the 29th International Conference on Computational Linguistics, 2022, pp. 3296–3308.
Bondarenko, Alexander, et al. “CausalQA: A Benchmark for Causal Question Answering.” Proceedings of the 29th International Conference on Computational Linguistics, International Committee on Computational Linguistics, 2022, pp. 3296–3308.
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