Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Causality-Guided Prediction of the TED Talk Ratings from the
  Speech-Transcripts using Neural Networks   Automated prediction of public speaking performance enables novel systems for
tutoring public speaking skills. We use the largest open repository---TED
Talks---to predict the ratings provided by the online viewers. The dataset
contains over 2200 talk transcripts and the associated meta information
including over 5.5 million ratings from spontaneous visitors to the website. We
carefully removed the bias present in the dataset (e.g., the speakers'
reputations, popularity gained by publicity, etc.) by modeling the data
generating process using a causal diagram. We use a word sequence based
recurrent architecture and a dependency tree based recursive architecture as
the neural networks for predicting the TED talk ratings. Our neural network
models can predict the ratings with an average F-score of 0.77 which largely
outperforms the competitive baseline method.

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The categories of this paper are: comp-gas
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The categories of this paper are: adap-org
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The categories of this paper are: eess
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The categories of this paper are: gr-qc
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The categories of this paper are: bayes-an
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-alg
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The categories of this paper are: solv-int
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The categories of this paper are: math-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-lat
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The categories of this paper are: chem-ph
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The categories of this paper are: funct-an