Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving coreference resolution with automatically predicted prosodic
  information   Adding manually annotated prosodic information, specifically pitch accents
and phrasing, to the typical text-based feature set for coreference resolution
has previously been shown to have a positive effect on German data. Practical
applications on spoken language, however, would rely on automatically predicted
prosodic information. In this paper we predict pitch accents (and phrase
boundaries) using a convolutional neural network (CNN) model from acoustic
features extracted from the speech signal. After an assessment of the quality
of these automatic prosodic annotations, we show that they also significantly
improve coreference resolution.

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The categories of this paper are: plasm-ph
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The categories of this paper are: supr-con
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The categories of this paper are: atom-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: bayes-an
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The categories of this paper are: acc-phys
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-lat
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The categories of this paper are: math
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The categories of this paper are: chem-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: adap-org
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: dg-ga