# Fine-grained Information Status Classification Using Discourse Context-Aware Self-Attention

## TL;DR

This paper introduces a discourse context-aware self-attention neural network utilizing BERT for fine-grained information status classification, achieving state-of-the-art results and improving bridging anaphora recognition without relying on hand-crafted features.

## Contribution

It presents a novel neural model that leverages discourse context and BERT representations for improved information status classification and bridging anaphora recognition.

## Key findings

- Achieves 4.1% accuracy improvement over previous methods.
- Improves F1 score for bridging anaphora recognition by 3.9%.
- Eliminates need for complex hand-crafted semantic features.

## 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 discourse context-aware self-attention neural network model for fine-grained IS classification. On the ISNotes corpus (Markert et al., 2012), our model with the contextually-encoded word representations (BERT) (Devlin et al., 2018) achieves new state-of-the-art performances on fine-grained IS classification, obtaining a 4.1% absolute overall accuracy improvement compared to Hou et al. (2013a). More importantly, we also show an improvement of 3.9% F1 for bridging anaphora recognition without using any complex hand-crafted semantic features designed for capturing the bridging phenomenon.