End-to-end Neural Information Status Classification

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., 2013a; 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 Furthermore, our system performs significantly better than other baselines for both mention extraction and finegrained IS classification in the end-to-end setting. Finally, we apply our system on BASHI We find that our end-to-end system trained on IS-Notes 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 indomain datasets (Yu and Poesio, 2020).