Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: TriggerNER: Learning with Entity Triggers as Explanations for Named
  Entity Recognition   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.

17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph