@inproceedings{ren-etal-2020-towards,
    title = "Towards Interpretable Reasoning over Paragraph Effects in Situation",
    author = "Ren, Mucheng  and
      Geng, Xiubo  and
      Qin, Tao  and
      Huang, Heyan  and
      Jiang, Daxin",
    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.548/",
    doi = "10.18653/v1/2020.emnlp-main.548",
    pages = "6745--6758",
    abstract = "We focus on the task of reasoning over paragraph effects in situation, which requires a model to understand the cause and effect described in a background paragraph, and apply the knowledge to a novel situation. Existing works ignore the complicated reasoning process and solve it with a one-step {\textquotedblleft}black box{\textquotedblright} model. Inspired by human cognitive processes, in this paper we propose a sequential approach for this task which explicitly models each step of the reasoning process with neural network modules. In particular, five reasoning modules are designed and learned in an end-to-end manner, which leads to a more interpretable model. Experimental results on the ROPES dataset demonstrate the effectiveness and explainability of our proposed approach."
}