Multi-class Hierarchical Question Classification for Multiple Choice Science Exams

Dongfang Xu, Peter Jansen, Jaycie Martin, Zhengnan Xie, Vikas Yadav, Harish Tayyar Madabushi, Oyvind Tafjord, Peter Clark


Abstract
Prior work has demonstrated that question classification (QC), recognizing the problem domain of a question, can help answer it more accurately. However, developing strong QC algorithms has been hindered by the limited size and complexity of annotated data available. To address this, we present the largest challenge dataset for QC, containing 7,787 science exam questions paired with detailed classification labels from a fine-grained hierarchical taxonomy of 406 problem domains. We then show that a BERT-based model trained on this dataset achieves a large (+0.12 MAP) gain compared with previous methods, while also achieving state-of-the-art performance on benchmark open-domain and biomedical QC datasets. Finally, we show that using this model’s predictions of question topic significantly improves the accuracy of a question answering system by +1.7% P@1, with substantial future gains possible as QC performance improves.
Anthology ID:
2020.lrec-1.661
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
5370–5382
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.661
DOI:
Bibkey:
Cite (ACL):
Dongfang Xu, Peter Jansen, Jaycie Martin, Zhengnan Xie, Vikas Yadav, Harish Tayyar Madabushi, Oyvind Tafjord, and Peter Clark. 2020. Multi-class Hierarchical Question Classification for Multiple Choice Science Exams. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 5370–5382, Marseille, France. European Language Resources Association.
Cite (Informal):
Multi-class Hierarchical Question Classification for Multiple Choice Science Exams (Xu et al., LREC 2020)
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PDF:
https://aclanthology.org/2020.lrec-1.661.pdf
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