TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition

• URL: http://arxiv.org/abs/2004.07493v4
• Date: Tue, 7 Jul 2020 01:10:11 GMT
• Title: TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition
• Authors: Bill Yuchen Lin, Dong-Ho Lee, Ming Shen, Ryan Moreno, Xiao Huang, Prashant Shiralkar, Xiang Ren
• Abstract summary: We introduce "entity triggers," an effective proxy of human explanations for facilitating label-efficient learning of NER models. 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.
• Score: 42.984048204280676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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