Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Hybrid Neural Network Model for Commonsense Reasoning   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 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.

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The categories of this paper are: alg-geom
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The categories of this paper are: ao-sci
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The categories of this paper are: chem-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: physics
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The categories of this paper are: atom-ph
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The categories of this paper are: q-alg
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The categories of this paper are: hep-th
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The categories of this paper are: mtrl-th
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The categories of this paper are: math-ph
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The categories of this paper are: adap-org
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The categories of this paper are: math
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The categories of this paper are: hep-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-lat
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The categories of this paper are: q-bio