1 research outputs found
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.Comment: 10 pages (8 pages + 2 pages of references), 1 figure, 7 table