Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Neural-Symbolic Approach to Natural Language Understanding   Deep neural networks, empowered by pre-trained language models, have achieved
remarkable results in natural language understanding (NLU) tasks. However,
their performances can deteriorate drastically when logical reasoning is needed
in the process. This is because, ideally, NLU needs to depend on not only
analogical reasoning, which deep neural networks are good at, but also logical
reasoning. According to the dual-process theory, analogical reasoning and
logical reasoning are respectively carried out by System 1 and System 2 in the
human brain. Inspired by the theory, we present a novel framework for NLU
called Neural-Symbolic Processor (NSP), which performs analogical reasoning
based on neural processing and performs logical reasoning based on both neural
and symbolic processing. As a case study, we conduct experiments on two NLU
tasks, question answering (QA) and natural language inference (NLI), when
numerical reasoning (a type of logical reasoning) is necessary. The
experimental results show that our method significantly outperforms
state-of-the-art methods in both tasks.

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The categories of this paper are: nlin
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The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
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The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
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The categories of this paper are: nucl-ex
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The categories of this paper are: econ
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The categories of this paper are: eess
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The categories of this paper are: hep-ex
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The categories of this paper are: mtrl-th
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The categories of this paper are: comp-gas
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The categories of this paper are: acc-phys