%0 Conference Paper %1 bondarenko-etal-2022-causalqa %A Bondarenko, Alexander %A Wolska, Magdalena %A Heindorf, Stefan %A Blübaum, Lukas %A Ngonga Ngomo, Axel-Cyrille %A Stein, Benno %A Braslavski, Pavel %A Hagen, Matthias %A Potthast, Martin %B Proceedings of the 29th International Conference on Computational Linguistics %C Gyeongju, Republic of Korea %D 2022 %I International Committee on Computational Linguistics %K %P 3296--3308 %T CausalQA: A Benchmark for Causal Question Answering %U https://aclanthology.org/2022.coling-1.291 %X 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.

@inproceedings{bondarenko-etal-2022-causalqa, 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.}, added-at = {2024-09-10T11:56:37.000+0200}, address = {Gyeongju, Republic of Korea}, author = {Bondarenko, Alexander and Wolska, Magdalena and Heindorf, Stefan and Bl{\"u}baum, Lukas and Ngonga Ngomo, Axel-Cyrille and Stein, Benno and Braslavski, Pavel and Hagen, Matthias and Potthast, Martin}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2eac699710e7b84a9fc8fa6414eef9474/scadsfct}, booktitle = {Proceedings of the 29th International Conference on Computational Linguistics}, interhash = {1e4fba15b489d16ad8f2ac3ca2665c45}, intrahash = {eac699710e7b84a9fc8fa6414eef9474}, keywords = {}, month = oct, pages = {3296--3308}, publisher = {International Committee on Computational Linguistics}, timestamp = {2024-09-10T15:15:57.000+0200}, title = {{C}ausal{QA}: A Benchmark for Causal Question Answering}, url = {https://aclanthology.org/2022.coling-1.291}, year = 2022 }