Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-task Learning Based Neural Bridging Reference Resolution   We propose a multi task learning-based neural model for resolving bridging
references tackling two key challenges. The first challenge is the lack of
large corpora annotated with bridging references. To address this, we use
multi-task learning to help bridging reference resolution with coreference
resolution. We show that substantial improvements of up to 8 p.p. can be
achieved on full bridging resolution with this architecture. The second
challenge is the different definitions of bridging used in different corpora,
meaning that hand-coded systems or systems using special features designed for
one corpus do not work well with other corpora. Our neural model only uses a
small number of corpus independent features, thus can be applied to different
corpora. Evaluations with very different bridging corpora (ARRAU, ISNOTES,
BASHI and SCICORP) suggest that our architecture works equally well on all
corpora, and achieves the SoTA results on full bridging resolution for all
corpora, outperforming the best reported results by up to 36.3 p.p..

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The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol