Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Natural Language Inference with a Pretrained Parser   We introduce a novel approach to incorporate syntax into natural language
inference (NLI) models. Our method uses contextual token-level vector
representations from a pretrained dependency parser. Like other contextual
embedders, our method is broadly applicable to any neural model. We experiment
with four strong NLI models (decomposable attention model, ESIM, BERT, and
MT-DNN), and show consistent benefit to accuracy across three NLI benchmarks.

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The categories of this paper are: supr-con
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The categories of this paper are: dg-ga
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The categories of this paper are: eess
16
The categories of this paper are: stat
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The categories of this paper are: mtrl-th
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The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
10
The categories of this paper are: q-bio
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The categories of this paper are: solv-int
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The categories of this paper are: adap-org
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: cmp-lg
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The categories of this paper are: atom-ph
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The categories of this paper are: bayes-an