  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.
