Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fine-grained Information Status Classification Using Discourse
  Context-Aware Self-Attention   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.

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The categories of this paper are: eess
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: cond-mat
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The categories of this paper are: astro-ph
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The categories of this paper are: funct-an
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The categories of this paper are: chem-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: nlin
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The categories of this paper are: ao-sci
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The categories of this paper are: q-fin
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The categories of this paper are: cmp-lg
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The categories of this paper are: stat
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The categories of this paper are: alg-geom
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The categories of this paper are: comp-gas
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The categories of this paper are: q-bio