Corpus ID: 199668863

Multi-class Hierarchical Question Classification for Multiple Choice Science Exams

@article{Xu2020MulticlassHQ,
  title={Multi-class Hierarchical Question Classification for Multiple Choice Science Exams},
  author={Dongfang Xu and Peter Jansen and Jaycie Martin and Zhengnan Xie and Vikas Yadav and Harish Tayyar Madabushi and Oyvind Tafjord and Peter Clark},
  journal={ArXiv},
  year={2020},
  volume={abs/1908.05441}
}
  • Dongfang Xu, Peter Jansen, +5 authors Peter Clark
  • Published 2020
  • Computer Science
  • ArXiv
  • Prior work has demonstrated that question classification (QC), recognizing the problem domain of a question, can help answer it more accurately. [...] Key Method 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…Expand Abstract

    Figures, Tables, and Topics from this paper.

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 59 REFERENCES

    An ontology for clinical questions about the contents of patient notes

    VIEW 3 EXCERPTS

    Biomedical Question Types Classification using syntactic and rule based approach

    VIEW 1 EXCERPT