TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition
Bill Yuchen Lin, Dong-Ho Lee, Ming Shen, Ryan Moreno, Xiao Huang, Prashant Shiralkar, Xiang Ren · 2020
Training neural models for named entity recognition (NER) in a new domain often requires additional human annotations 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 1 .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.The framework is significantly more cost-effective than the traditional frameworks.