  Most previous studies on information status (IS) classification and bridging
anaphora recognition assume that the gold mention or syntactic tree information
is given (Hou et al., 2013; Roesiger et al., 2018; Hou, 2020; Yu and Poesio,
2020). In this paper, we propose an end-to-end neural approach for information
status classification. Our approach consists of a mention extraction component
and an information status assignment component. During the inference time, our
system takes a raw text as the input and generates mentions together with their
information status. On the ISNotes corpus (Markert et al., 2012), we show that
our information status assignment component achieves new state-of-the-art
results on fine-grained IS classification based on gold mentions. Furthermore,
our system performs significantly better than other baselines for both mention
extraction and fine-grained IS classification in the end-to-end setting.
Finally, we apply our system on BASHI (Roesiger, 2018) and SciCorp (Roesiger,
2016) to recognize referential bridging anaphora. We find that our end-to-end
system trained on ISNotes achieves competitive results on bridging anaphora
recognition compared to the previous state-of-the-art system that relies on
syntactic information and is trained on the in-domain datasets (Yu and Poesio,
2020).
