Deep neural models for low-resource named entity recognition (NER) have shown
impressive results by leveraging distant super-vision or other meta-level
information (e.g. explanation). However, the costs of acquiring such additional
information are generally prohibitive, especially in domains where existing
resources (e.g. databases to be used for distant supervision) may not exist. In
this paper, we present a novel two-stage framework (AutoTriggER) to improve NER
performance by automatically generating and leveraging "entity triggers" which
are essentially human-readable clues in the text that can help guide the model
to make better decisions. Thus, the framework is able to both create and
leverage auxiliary supervision by itself. Through experiments on three
well-studied NER datasets, we show that our automatically extracted triggers
are well-matched to human triggers, and AutoTriggER improves performance over a
RoBERTa-CRFarchitecture by nearly 0.5 F1 points on average and much more in a
low resource setting.

本文提出了一个新的两阶段框架（AutoTriggER），通过自动生成和利用文本中的 “实体触发器” 来改善命名实体识别的性能，从而能够创建并利用辅助监督。在三个公认的命名实体识别数据集上的实验表明，我们自动提取的触发器与人工触发器非常匹配，AutoTriggER 在平均 0.5 F1 点低的资源设置上比 RoBERTa-CRF 架构有更优的性能表现。

AutoTriggER: 带辅助触发词提取的命名实体识别

AutoTriggER: Named Entity Recognition with Auxiliary Trigger Extraction

Training neural models for named entity recognition (NER) in a new domain
often requires additional human annotations (e.g., tens of thousands of labeled
instances) that are usually expensive and time-consuming to collect. Thus, a
crucial research question is how to obtain supervision in a cost-effective way.
In this paper, we introduce "entity triggers," an effective proxy of human
explanations for facilitating label-efficient learning of NER models. An entity
trigger is defined as a group of words in a sentence that helps to explain why
humans would recognize an entity in the sentence.
We crowd-sourced 14k entity triggers for two well-studied NER datasets. Our
proposed model, Trigger Matching Network, jointly learns trigger
representations and soft matching module with self-attention such that can
generalize to unseen sentences easily for tagging. Our framework is
significantly more cost-effective than the traditional neural NER frameworks.
Experiments show that using only 20% of the trigger-annotated sentences results
in a comparable performance as using 70% of conventional annotated sentences.

本文提出一种名为 'entity triggers' 的新方法，通过众包收集注释以减少训练命名实体识别器所需的标注数量，使用自我注意力机制进行软匹配和泛化，提高成本效益，相比传统的神经 NER 框架，只使用注释的 20% 即可获得与 70% 传统标注相当的性能。