  Zero-shot entity and relation classification models leverage available
external information of unseen classes -- e.g., textual descriptions -- to
annotate input text data. Thanks to the minimum data requirement, Zero-Shot
Learning (ZSL) methods have high value in practice, especially in applications
where labeled data is scarce. Even though recent research in ZSL has
demonstrated significant results, our analysis reveals that those methods are
sensitive to provided textual descriptions of entities (or relations). Even a
minor modification of descriptions can lead to a change in the decision
boundary between entity (or relation) classes. In this paper, we formally
define the problem of identifying effective descriptions for zero shot
inference. We propose a strategy for generating variations of an initial
description, a heuristic for ranking them and an ensemble method capable of
boosting the predictions of zero-shot models through description enhancement.
Empirical results on four different entity and relation classification datasets
show that our proposed method outperform existing approaches and achieve new
SOTA results on these datasets under the ZSL settings. The source code of the
proposed solutions and the evaluation framework are open-sourced.
