@inproceedings{he-etal-2019-hybrid,
    title = "A Hybrid Neural Network Model for Commonsense Reasoning",
    author = "He, Pengcheng  and
      Liu, Xiaodong  and
      Chen, Weizhu  and
      Gao, Jianfeng",
    editor = "Ostermann, Simon  and
      Zhang, Sheng  and
      Roth, Michael  and
      Clark, Peter",
    booktitle = "Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D19-6002",
    doi = "10.18653/v1/D19-6002",
    pages = "13--21",
    abstract = "This paper proposes a hybrid neural network(HNN) model for commonsense reasoning. An HNN consists of two component models, a masked language model and a semantic similarity model, which share a BERTbased contextual encoder but use different model-specific input and output layers. HNN obtains new state-of-the-art results on three classic commonsense reasoning tasks, pushing the WNLI benchmark to 89{\%}, the Winograd Schema Challenge (WSC) benchmark to 75.1{\%}, and the PDP60 benchmark to 90.0{\%}. An ablation study shows that language models and semantic similarity models are complementary approaches to commonsense reasoning, and HNN effectively combines the strengths of both. The code and pre-trained models will be publicly available at https: //github.com/namisan/mt-dnn.",
}