Corpus ID: 216641773

AxCell: Automatic Extraction of Results from Machine Learning Papers

@article{Kardas2020AxCellAE,
  title={AxCell: Automatic Extraction of Results from Machine Learning Papers},
  author={M. Kardas and Piotr Czapla and Pontus Stenetorp and Sebastian Ruder and Sebastian Riedel and R. Taylor and Robert Stojnic},
  journal={ArXiv},
  year={2020},
  volume={abs/2004.14356}
}
  • M. Kardas, Piotr Czapla, +4 authors Robert Stojnic
  • Published 2020
  • Computer Science, Mathematics
  • ArXiv
  • Tracking progress in machine learning has become increasingly difficult with the recent explosion in the number of papers. In this paper, we present AxCell, an automatic machine learning pipeline for extracting results from papers. AxCell uses several novel components, including a table segmentation subtask, to learn relevant structural knowledge that aids extraction. When compared with existing methods, our approach significantly improves the state of the art for results extraction. We also… CONTINUE READING

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 17 REFERENCES
    Measurement of metabolic rate in Drosophila using respirometry.
    24
    Fabrication and photoluminiscent properties of titanium oxide nanotube arrays
    45
    The physician's role in preventing alcohol and drug abuse.
    2
    Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction
    7
    Tracking the Progress in Natural Language Processing
    • 2018
    fastai: A Layered API for Deep Learning
    46