  Few-shot and zero-shot text classification aim to recognize samples from
novel classes with limited labeled samples or no labeled samples at all. While
prevailing methods have shown promising performance via transferring knowledge
from seen classes to unseen classes, they are still limited by (1) Inherent
dissimilarities among classes make the transformation of features learned from
seen classes to unseen classes both difficult and inefficient. (2) Rare labeled
novel samples usually cannot provide enough supervision signals to enable the
model to adjust from the source distribution to the target distribution,
especially for complicated scenarios. To alleviate the above issues, we propose
a simple and effective strategy for few-shot and zero-shot text classification.
We aim to liberate the model from the confines of seen classes, thereby
enabling it to predict unseen categories without the necessity of training on
seen classes. Specifically, for mining more related unseen category knowledge,
we utilize a large pre-trained language model to generate pseudo novel samples,
and select the most representative ones as category anchors. After that, we
convert the multi-class classification task into a binary classification task
and use the similarities of query-anchor pairs for prediction to fully leverage
the limited supervision signals. Extensive experiments on six widely used
public datasets show that our proposed method can outperform other strong
baselines significantly in few-shot and zero-shot tasks, even without using any
seen class samples.
