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- W3117935238 abstract "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 simple discourse context-aware BERT model for fine-grained IS classification. On the ISNotes corpus (Markert et al., 2012), our model achieves new state-of-the-art performance on fine-grained IS classification, obtaining a 4.8 absolute overall accuracy improvement compared to Hou et al. (2013a). More importantly, we also show an improvement of 10.5 F1 points for bridging anaphora recognition without using any complex hand-crafted semantic features designed for capturing the bridging phenomenon. We further analyze the trained model and find that the most attended signals for each IS category correspond well to linguistic notions of information status." @default.
- W3117935238 created "2021-01-05" @default.
- W3117935238 creator A5078812348 @default.
- W3117935238 date "2020-01-01" @default.
- W3117935238 modified "2023-09-25" @default.
- W3117935238 title "Fine-grained Information Status Classification Using Discourse Context-Aware BERT" @default.
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- W3117935238 doi "https://doi.org/10.18653/v1/2020.coling-main.537" @default.
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