A Hybrid Neural Network Model for Commonsense Reasoning

@article{He2019AHN,
  title={A Hybrid Neural Network Model for Commonsense Reasoning},
  author={Pengcheng He and Xiaodong Liu and Weizhu Chen and Jianfeng Gao},
  journal={ArXiv},
  year={2019},
  volume={abs/1907.11983}
}
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 BERT-based 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… CONTINUE READING

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