Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Instance-Based Neural Dependency Parsing   Interpretable rationales for model predictions are crucial in practical
applications. We develop neural models that possess an interpretable inference
process for dependency parsing. Our models adopt instance-based inference,
where dependency edges are extracted and labeled by comparing them to edges in
a training set. The training edges are explicitly used for the predictions;
thus, it is easy to grasp the contribution of each edge to the predictions. Our
experiments show that our instance-based models achieve competitive accuracy
with standard neural models and have the reasonable plausibility of
instance-based explanations.

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The categories of this paper are: cond-mat
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The categories of this paper are: gr-qc
28
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: stat
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The categories of this paper are: adap-org
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The categories of this paper are: funct-an
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The categories of this paper are: quant-ph
2
The categories of this paper are: math
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The categories of this paper are: mtrl-th
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The categories of this paper are: chem-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: math-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: dg-ga
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The categories of this paper are: bayes-an