Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Cross-Task Analysis of Text Span Representations   Many natural language processing (NLP) tasks involve reasoning with textual
spans, including question answering, entity recognition, and coreference
resolution. While extensive research has focused on functional architectures
for representing words and sentences, there is less work on representing
arbitrary spans of text within sentences. In this paper, we conduct a
comprehensive empirical evaluation of six span representation methods using
eight pretrained language representation models across six tasks, including two
tasks that we introduce. We find that, although some simple span
representations are fairly reliable across tasks, in general the optimal span
representation varies by task, and can also vary within different facets of
individual tasks. We also find that the choice of span representation has a
bigger impact with a fixed pretrained encoder than with a fine-tuned encoder.

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The categories of this paper are: nlin
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-th
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The categories of this paper are: cond-mat
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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: physics
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The categories of this paper are: chao-dyn
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The categories of this paper are: stat
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The categories of this paper are: atom-ph
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The categories of this paper are: adap-org
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The categories of this paper are: cmp-lg
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: solv-int