End-to-end Neural Information Status Classification

Yufang Hou


Abstract
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).
Anthology ID:
2021.findings-emnlp.119
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venue:
Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1377–1388
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.119
DOI:
10.18653/v1/2021.findings-emnlp.119
Bibkey:
Cite (ACL):
Yufang Hou. 2021. End-to-end Neural Information Status Classification. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1377–1388, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
End-to-end Neural Information Status Classification (Hou, Findings 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.119.pdf
Video:
 https://aclanthology.org/2021.findings-emnlp.119.mp4
Code
 IBM/bridging-resolution