  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..
