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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
; | | Date: |
16 Apr 2020 | | 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, named Trigger Matching Network, jointly learns trigger
representations and soft matching module with self-attention such that can
generalize to unseen sentences easily for tagging. Experiments show that the
framework is significantly more cost-effective such that using 20% of the
trigger-annotated sentences can result in a comparable performance of
conventional supervised approaches using 70% training data. We publicly release
the collected entity triggers and our code. | | Source: | arXiv, 2004.7493 | | Services: | Forum | Review | PDF | Favorites | |
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