Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Contextualized Word Representations for Reading Comprehension   Reading a document and extracting an answer to a question about its content
has attracted substantial attention recently. While most work has focused on
the interaction between the question and the document, in this work we evaluate
the importance of context when the question and document are processed
independently. We take a standard neural architecture for this task, and show
that by providing rich contextualized word representations from a large
pre-trained language model as well as allowing the model to choose between
context-dependent and context-independent word representations, we can obtain
dramatic improvements and reach performance comparable to state-of-the-art on
the competitive SQuAD dataset.

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The categories of this paper are: q-alg
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The categories of this paper are: patt-sol
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The categories of this paper are: dg-ga
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The categories of this paper are: solv-int
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The categories of this paper are: funct-an
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The categories of this paper are: econ
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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: nucl-th
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The categories of this paper are: hep-lat
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The categories of this paper are: math-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: atom-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: stat
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The categories of this paper are: cond-mat