Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adapting Coreference Resolution Models through Active Learning   Neural coreference resolution models trained on one dataset may not transfer
to new, low-resource domains. Active learning mitigates this problem by
sampling a small subset of data for annotators to label. While active learning
is well-defined for classification tasks, its application to coreference
resolution is neither well-defined nor fully understood. This paper explores
how to actively label coreference, examining sources of model uncertainty and
document reading costs. We compare uncertainty sampling strategies and their
advantages through thorough error analysis. In both synthetic and human
experiments, labeling spans within the same document is more effective than
annotating spans across documents. The findings contribute to a more realistic
development of coreference resolution models.

15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th