Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Few-shot Slot Tagging with Collapsed Dependency Transfer and
  Label-enhanced Task-adaptive Projection Network   In this paper, we explore the slot tagging with only a few labeled support
sentences (a.k.a. few-shot). Few-shot slot tagging faces a unique challenge
compared to the other few-shot classification problems as it calls for modeling
the dependencies between labels. But it is hard to apply previously learned
label dependencies to an unseen domain, due to the discrepancy of label sets.
To tackle this, we introduce a collapsed dependency transfer mechanism into the
conditional random field (CRF) to transfer abstract label dependency patterns
as transition scores. In the few-shot setting, the emission score of CRF can be
calculated as a word's similarity to the representation of each label. To
calculate such similarity, we propose a Label-enhanced Task-Adaptive Projection
Network (L-TapNet) based on the state-of-the-art few-shot classification model
-- TapNet, by leveraging label name semantics in representing labels.
Experimental results show that our model significantly outperforms the
strongest few-shot learning baseline by 14.64 F1 scores in the one-shot
setting.

23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
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The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
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The categories of this paper are: nucl-ex
2
The categories of this paper are: math