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.

翻译：深度神经模型在命名实体识别（NER）任务中，通过利用远程监督和解释等辅助信息，在克服标签稀缺性和泛化未见实体方面取得了显著成果。然而，获取此类额外信息的成本通常过高。本文提出了一种新颖的两阶段框架（AutoTriggER），通过自动生成并利用文本中可读的"实体触发词"（即引导模型做出更优决策的线索）来提升NER性能。该框架利用事后解释生成合理性依据，并通过嵌入插值技术增强模型的先验知识。此方法使模型能够利用触发词推断实体边界与类型，而非仅依赖记忆实体词汇本身。我们在三个广泛研究的NER数据集上进行了实验，结果表明AutoTriggER具有强标签效率、能够泛化未见实体，且平均F1值较RoBERTa-CRF基线提升近0.5个点。