  Deep neural models for named entity recognition (NER) have shown impressive
results in overcoming label scarcity and generalizing to unseen entities by
leveraging distant supervision and auxiliary information such as explanations.
However, the costs of acquiring such additional information are generally
prohibitive. In this paper, we present a novel two-stage framework
(AutoTriggER) to improve NER performance by automatically generating and
leveraging ``entity triggers'' which are human-readable cues in the text that
help guide the model to make better decisions. Our framework leverages post-hoc
explanation to generate rationales and strengthens a model's prior knowledge
using an embedding interpolation technique. This approach allows models to
exploit triggers to infer entity boundaries and types instead of solely
memorizing the entity words themselves. Through experiments on three
well-studied NER datasets, AutoTriggER shows strong label-efficiency, is
capable of generalizing to unseen entities, and outperforms the RoBERTa-CRF
baseline by nearly 0.5 F1 points on average.
