Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-Label Few-Shot Learning for Aspect Category Detection   Aspect category detection (ACD) in sentiment analysis aims to identify the
aspect categories mentioned in a sentence. In this paper, we formulate ACD in
the few-shot learning scenario. However, existing few-shot learning approaches
mainly focus on single-label predictions. These methods can not work well for
the ACD task since a sentence may contain multiple aspect categories.
Therefore, we propose a multi-label few-shot learning method based on the
prototypical network. To alleviate the noise, we design two effective attention
mechanisms. The support-set attention aims to extract better prototypes by
removing irrelevant aspects. The query-set attention computes multiple
prototype-specific representations for each query instance, which are then used
to compute accurate distances with the corresponding prototypes. To achieve
multi-label inference, we further learn a dynamic threshold per instance by a
policy network. Extensive experimental results on three datasets demonstrate
that the proposed method significantly outperforms strong baselines.

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The categories of this paper are: mtrl-th
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The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
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The categories of this paper are: nlin
3
The categories of this paper are: physics
18
The categories of this paper are: econ
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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: eess
33
The categories of this paper are: plasm-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: solv-int
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The categories of this paper are: cond-mat
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The categories of this paper are: comp-gas
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The categories of this paper are: adap-org
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The categories of this paper are: chem-ph