@inproceedings{omura-etal-2020-method,
    title = "A Method for Building a Commonsense Inference Dataset based on Basic Events",
    author = "Omura, Kazumasa  and
      Kawahara, Daisuke  and
      Kurohashi, Sadao",
    editor = "Webber, Bonnie  and
      Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.emnlp-main.192/",
    doi = "10.18653/v1/2020.emnlp-main.192",
    pages = "2450--2460",
    abstract = "We present a scalable, low-bias, and low-cost method for building a commonsense inference dataset that combines automatic extraction from a corpus and crowdsourcing. Each problem is a multiple-choice question that asks contingency between basic events. We applied the proposed method to a Japanese corpus and acquired 104k problems. While humans can solve the resulting problems with high accuracy (88.9{\%}), the accuracy of a high-performance transfer learning model is reasonably low (76.0{\%}). We also confirmed through dataset analysis that the resulting dataset contains low bias. We released the datatset to facilitate language understanding research."
}