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


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.
Anthology ID:
2020.acl-main.752
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8503–8511
Language:
URL:
https://aclanthology.org/2020.acl-main.752
DOI:
10.18653/v1/2020.acl-main.752
Bibkey:
Cite (ACL):
Bill Yuchen Lin, Dong-Ho Lee, Ming Shen, Ryan Moreno, Xiao Huang, Prashant Shiralkar, and Xiang Ren. 2020. TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8503–8511, Online. Association for Computational Linguistics.
Cite (Informal):
TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition (Lin et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.752.pdf
Video:
 http://slideslive.com/38929157
Code
 INK-USC/TriggerNER
Data
BC5CDRCoNLL-2003