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        <title>A Hybrid Neural Network Model for Commonsense Reasoning</title>
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    <name type="personal">
        <namePart type="given">Pengcheng</namePart>
        <namePart type="family">He</namePart>
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    <name type="personal">
        <namePart type="given">Xiaodong</namePart>
        <namePart type="family">Liu</namePart>
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        <namePart type="given">Weizhu</namePart>
        <namePart type="family">Chen</namePart>
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    <name type="personal">
        <namePart type="given">Jianfeng</namePart>
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            <title>Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing</title>
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            <namePart type="given">Simon</namePart>
            <namePart type="family">Ostermann</namePart>
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            <namePart type="given">Sheng</namePart>
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            <namePart type="given">Michael</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/D19-6002</identifier>
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        <date>2019-11</date>
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%0 Conference Proceedings
%T A Hybrid Neural Network Model for Commonsense Reasoning
%A He, Pengcheng
%A Liu, Xiaodong
%A Chen, Weizhu
%A Gao, Jianfeng
%Y Ostermann, Simon
%Y Zhang, Sheng
%Y Roth, Michael
%Y Clark, Peter
%S Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F he-etal-2019-hybrid
%X 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.
%R 10.18653/v1/D19-6002
%U https://aclanthology.org/D19-6002
%U https://doi.org/10.18653/v1/D19-6002
%P 13-21

Markdown (Informal)

[A Hybrid Neural Network Model for Commonsense Reasoning](https://aclanthology.org/D19-6002) (He et al., 2019)

• A Hybrid Neural Network Model for Commonsense Reasoning (He et al., 2019)

ACL

• Pengcheng He, Xiaodong Liu, Weizhu Chen, and Jianfeng Gao. 2019. A Hybrid Neural Network Model for Commonsense Reasoning. In Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing, pages 13–21, Hong Kong, China. Association for Computational Linguistics.