The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data
Open accessAbstract
Existing question answering datasets focus on dealing with homogeneous information, based either only on text or KB/Table information alone.However, as human knowledge is distributed over heterogeneous forms, using homogeneous information alone might lead to severe coverage problems.To fill in the gap, we present HybridQA 1 , a new large-scale question-answering dataset that requires reasoning on heterogeneous information.Each question is aligned with a Wikipedia table and multiple free-form corpora linked with the entities in the table.The questions are designed to aggregate both tabular information and text information, i.e., lack of either form would render the question unanswerable.We test with three different models: 1) a table-only model.2) text-only model.3) a hybrid model that combines heterogeneous information to find the answer.The experimental results show that the EM scores obtained by two baselines are below 20%, while the hybrid model can achieve an EM over 40%.This gap suggests the necessity to aggregate heterogeneous information in HybridQA.However, the hybrid model's score is still far behind human performance.Hence, HybridQA can serve as a challenging benchmark to study question answering with heterogeneous information.
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Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
SQuAD: 100,000+ Questions for Machine Comprehension of Text
Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1264 · Cited by 6,208
Natural Questions: A benchmark for question answering research
Kwiatkowski, T., Palomaki, J., Redfield, O., Collins, M., Parikh, A., Alberti, C., Epstein, D., Polosukhin, I., Devlin, J., Lee, K., et al. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00276 · Cited by 2,069
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W., Salakhutdinov, R., & Manning, C. D. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1259 · Cited by 1,804
TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Joshi, M., Choi, E., Weld, D., & Zettlemoyer, L. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1147 · Cited by 1,690
TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Joshi, M., Choi, E., Weld, D. S., & Zettlemoyer, L. (2017) · arXiv · doi:10.48550/arxiv.1705.03551 · Cited by 1,650
Reading Wikipedia to answer open-domain questions
Chen, D., Fisch, A., Weston, J., & Bordes, A. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1171 · Cited by 1,546
Yu, T., Zhang, R., Yang, K., Yasunaga, M., Wang, D., Li, Z., Ma, J., Li, I., Yao, Q., Roman, S., et al. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1425 · Cited by 1,034
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
Weston, J., Bordes, A., Chopra, S., Rush, A. M., van Merriënboer, B., Joulin, A., & Mikolov, T. (2015) · arXiv · doi:10.48550/arxiv.1502.05698 · Cited by 670
Cited by
♫ MuSiQue: Multihop Questions via Single-hop Question Composition
Trivedi, H., Balasubramanian, N., Khot, T., & Sabharwal, A. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00475 · Cited by 283
FinQA: A Dataset of Numerical Reasoning over Financial Data
Chen, Z., Chen, W., Smiley, C., Shah, S., Borova, I., Langdon, D., Moussa, R., Beane, M., Huang, T.-H., Routledge, B., et al. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.300 · Cited by 258
“As most existing QA datasets were constructed by MTurk workers (Yang et al., 2018; Dua et al., 2019; Chen et al., 2020c), it requires substantial domain-specific knowledge to compose meaningful questions that are hard for computers to answer.”
BackgroundMethodInfluentialSelf-citationConstructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps
Ho, X., Duong Nguyen, A.-K., Sugawara, S., & Aizawa, A. (2020) · Proceedings of the 28th International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.580 · Cited by 287
“Recently, Chen et al. (2020) introduced the HybridQA dataset—a multi-hop question answering over both tabular and textual data.”
BackgroundTAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance
Zhu, F., Lei, W., Huang, Y., Wang, C., Zhang, S., Lv, J., Feng, F., & Chua, T.-S. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.254 · Cited by 193
Sui, Y., Zhou, M., Zhou, M., Han, S., & Zhang, D. (2024) · Proceedings of the 17th ACM International Conference on Web Search and Data Mining · doi:10.1145/3616855.3635752 · Cited by 155
Metadata
- Authors
- Chen, W., Zha, H., Chen, Z., Xiong, W., Wang, H., & Wang, W. Y.
- Venue
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Published
- 2020
- Pages
- 1026-1036
- OpenAlex
- W3101082165
- Cited by
- 201
- References
- 17
- FWCI
- 14.31
- Type
- conference paper
- Language
- en
- Open access
- gold
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