Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Do We Need Neural Models to Explain Human Judgments of Acceptability?   Native speakers can judge whether a sentence is an acceptable instance of
their language. Acceptability provides a means of evaluating whether
computational language models are processing language in a human-like manner.
We test the ability of computational language models, simple language features,
and word embeddings to predict native English speakers judgments of
acceptability on English-language essays written by non-native speakers. We
find that much of the sentence acceptability variance can be captured by a
combination of features including misspellings, word order, and word similarity
(Pearson's r = 0.494). While predictive neural models fit acceptability
judgments well (r = 0.527), we find that a 4-gram model with statistical
smoothing is just as good (r = 0.528). Thanks to incorporating a count of
misspellings, our 4-gram model surpasses both the previous unsupervised
state-of-the art (Lau et al., 2015; r = 0.472), and the average non-expert
native speaker (r = 0.46). Our results demonstrate that acceptability is well
captured by n-gram statistics and simple language features.

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The categories of this paper are: cmp-lg
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The categories of this paper are: plasm-ph
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The categories of this paper are: econ
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The categories of this paper are: nucl-ex
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The categories of this paper are: solv-int
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The categories of this paper are: atom-ph
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The categories of this paper are: funct-an
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The categories of this paper are: acc-phys
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The categories of this paper are: chao-dyn
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The categories of this paper are: bayes-an
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The categories of this paper are: q-fin
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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: hep-lat
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The categories of this paper are: comp-gas
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The categories of this paper are: cond-mat