  Most previous studies on bridging anaphora resolution (Poesio et al., 2004;
Hou et al., 2013b; Hou, 2018a) use the pairwise model to tackle the problem and
assume that the gold mention information is given. In this paper, we cast
bridging anaphora resolution as question answering based on context. This
allows us to find the antecedent for a given anaphor without knowing any gold
mention information (except the anaphor itself). We present a question
answering framework (BARQA) for this task, which leverages the power of
transfer learning. Furthermore, we propose a novel method to generate a large
amount of "quasi-bridging" training data. We show that our model pre-trained on
this dataset and fine-tuned on a small amount of in-domain dataset achieves new
state-of-the-art results for bridging anaphora resolution on two bridging
corpora (ISNotes (Markert et al., 2012) and BASHI (Roesiger, 2018)).

Computation and Language