Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Tag-Enhanced Tree-Structured Neural Networks for Implicit Discourse
  Relation Classification   Identifying implicit discourse relations between text spans is a challenging
task because it requires understanding the meaning of the text. To tackle this
task, recent studies have tried several deep learning methods but few of them
exploited the syntactic information. In this work, we explore the idea of
incorporating syntactic parse tree into neural networks. Specifically, we
employ the Tree-LSTM model and Tree-GRU model, which are based on the tree
structure, to encode the arguments in a relation. Moreover, we further leverage
the constituent tags to control the semantic composition process in these
tree-structured neural networks. Experimental results show that our method
achieves state-of-the-art performance on PDTB corpus.

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The categories of this paper are: supr-con
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The categories of this paper are: stat
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The categories of this paper are: patt-sol
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The categories of this paper are: alg-geom
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The categories of this paper are: bayes-an
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: cmp-lg
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: eess
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The categories of this paper are: nlin
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The categories of this paper are: nucl-th
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The categories of this paper are: q-alg