Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Can Pre-trained Language Models Interpret Similes as Smart as Human?   Simile interpretation is a crucial task in natural language processing.
Nowadays, pre-trained language models (PLMs) have achieved state-of-the-art
performance on many tasks. However, it remains under-explored whether PLMs can
interpret similes or not. In this paper, we investigate the ability of PLMs in
simile interpretation by designing a novel task named Simile Property Probing,
i.e., to let the PLMs infer the shared properties of similes. We construct our
simile property probing datasets from both general textual corpora and
human-designed questions, containing 1,633 examples covering seven main
categories. Our empirical study based on the constructed datasets shows that
PLMs can infer similes' shared properties while still underperforming humans.
To bridge the gap with human performance, we additionally design a
knowledge-enhanced training objective by incorporating the simile knowledge
into PLMs via knowledge embedding methods. Our method results in a gain of
8.58% in the probing task and 1.37% in the downstream task of sentiment
classification. The datasets and code are publicly available at
https://github.com/Abbey4799/PLMs-Interpret-Simile.

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The categories of this paper are: quant-ph
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The categories of this paper are: math
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The categories of this paper are: ao-sci
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-th
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The categories of this paper are: solv-int
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The categories of this paper are: stat
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The categories of this paper are: physics
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The categories of this paper are: hep-lat
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The categories of this paper are: alg-geom
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The categories of this paper are: supr-con
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The categories of this paper are: nlin
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The categories of this paper are: patt-sol
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The categories of this paper are: cond-mat
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-ph