@inproceedings{lin-etal-2020-triggerner,
title = "{T}rigger{NER}: Learning with Entity Triggers as Explanations for Named Entity Recognition",
author = "Lin, Bill Yuchen and
Lee, Dong-Ho and
Shen, Ming and
Moreno, Ryan and
Huang, Xiao and
Shiralkar, Prashant and
Ren, Xiang",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.752",
doi = "10.18653/v1/2020.acl-main.752",
pages = "8503--8511",
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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%0 Conference Proceedings
%T TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition
%A Lin, Bill Yuchen
%A Lee, Dong-Ho
%A Shen, Ming
%A Moreno, Ryan
%A Huang, Xiao
%A Shiralkar, Prashant
%A Ren, Xiang
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F lin-etal-2020-triggerner
%X 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.
%R 10.18653/v1/2020.acl-main.752
%U https://aclanthology.org/2020.acl-main.752
%U https://doi.org/10.18653/v1/2020.acl-main.752
%P 8503-8511
Markdown (Informal)
[TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition](https://aclanthology.org/2020.acl-main.752) (Lin et al., ACL 2020)
ACL