  This paper studies the problem of detecting novel or unexpected instances in
text classification. In traditional text classification, the classes appeared
in testing must have been seen in training. However, in many applications, this
is not the case because in testing, we may see unexpected instances that are
not from any of the training classes. In this paper, we propose a significantly
more effective approach that converts the original problem to a pair-wise
matching problem and then outputs how probable two instances belong to the same
class. Under this approach, we present two models. The more effective model
uses two embedding matrices of a pair of instances as two channels of a CNN.
The output probabilities from such pairs are used to judge whether a test
instance is from a seen class or is novel/unexpected. Experimental results show
that the proposed method substantially outperforms the state-of-the-art
baselines.
