  Previous work on bridging anaphora recognition (Hou et al., 2013a) casts the
problem as a subtask of learning fine-grained information status (IS). However,
these systems heavily depend on many hand-crafted linguistic features. In this
paper, we propose a discourse context-aware self-attention neural network model
for fine-grained IS classification. On the ISNotes corpus (Markert et al.,
2012), our model with the contextually-encoded word representations (BERT)
(Devlin et al., 2018) achieves new state-of-the-art performances on
fine-grained IS classification, obtaining a 4.1% absolute overall accuracy
improvement compared to Hou et al. (2013a). More importantly, we also show an
improvement of 3.9% F1 for bridging anaphora recognition without using any
complex hand-crafted semantic features designed for capturing the bridging
phenomenon.
