Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Autoencoding Language Model Based Ensemble Learning for Commonsense
  Validation and Explanation   An ultimate goal of artificial intelligence is to build computer systems that
can understand human languages. Understanding commonsense knowledge about the
world expressed in text is one of the foundational and challenging problems to
create such intelligent systems. As a step towards this goal, we present in
this paper ALMEn, an Autoencoding Language Model based Ensemble learning method
for commonsense validation and explanation. By ensembling several advanced
pre-trained language models including RoBERTa, DeBERTa, and ELECTRA with
Siamese neural networks, our method can distinguish natural language statements
that are against commonsense (validation subtask) and correctly identify the
reason for making against commonsense (explanation selection subtask).
Experimental results on the benchmark dataset of SemEval-2020 Task 4 show that
our method outperforms state-of-the-art models, reaching 97.9% and 95.4%
accuracies on the validation and explanation selection subtasks, respectively.

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The categories of this paper are: physics
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The categories of this paper are: q-bio
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The categories of this paper are: supr-con
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The categories of this paper are: q-alg
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The categories of this paper are: solv-int
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The categories of this paper are: econ
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The categories of this paper are: quant-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: plasm-ph
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The categories of this paper are: funct-an
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The categories of this paper are: patt-sol
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The categories of this paper are: stat
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The categories of this paper are: hep-ex
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The categories of this paper are: gr-qc
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The categories of this paper are: cond-mat
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The categories of this paper are: chao-dyn