Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semi-Supervised Few-Shot Learning for Dual Question-Answer Extraction   This paper addresses the problem of key phrase extraction from sentences.
Existing state-of-the-art supervised methods require large amounts of annotated
data to achieve good performance and generalization. Collecting labeled data
is, however, often expensive. In this paper, we redefine the problem as
question-answer extraction, and present SAMIE: Self-Asking Model for
Information Ixtraction, a semi-supervised model which dually learns to ask and
to answer questions by itself. Briefly, given a sentence $s$ and an answer $a$,
the model needs to choose the most appropriate question $\hat q$; meanwhile,
for the given sentence $s$ and same question $\hat q$ selected in the previous
step, the model will predict an answer $\hat a$. The model can support few-shot
learning with very limited supervision. It can also be used to perform
clustering analysis when no supervision is provided. Experimental results show
that the proposed method outperforms typical supervised methods especially when
given little labeled data.

23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con