Gradience in Grammar: Experimental and Computational Aspects of Degrees of Grammaticality

Keller, Frank

doi:10.7282/t3gq6wms

Cited by 61 publications

(2 citation statements)

References 75 publications

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“…We used a single-trial, between-subjects design (see e.g., Morgan et al, 2020 Experiments 1 and 4; von der Malsburg et al, 2020 particularly the belief-estimation tasks). This choice was motivated by task adaptation effects which have been shown to emerge in a variety of experimental designs, both due to the repetition of critical trials (e.g., Balota et al, 2018;Demberg & Sayeed, 2016;Fine et al, 2013;Hammerly et al, 2019;Ness & Meltzer-Asscher, 2021;Pregla et al, 2021), and the inclusion of filler trials (e.g., Cowart, 1997;Keller, 2000;Schütze & Sprouse, 2013), commonly employed in within-subject designs (Arehalli & Wittenberg, 2021;…”

Section: Materials and Proceduresmentioning

confidence: 99%

Eventuality type predicts temporal order inferences in discourse comprehension

Marx,

Wittenberg

2024

Glossa Psycholinguistics

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One kind of temporal inference in discourse operates over iconicity, such that inferred temporal order follows reported order. In two preregistered experiments (combined N = 930), we asked whether this temporal inference is predictably modulated by linguistic eventuality. Based on event-structural theories of temporal interpretation, stative descriptions, corresponding to cognitively less salient states in the world, should serve as backgrounds for eventive descriptions, locating states earlier in time. Participants read descriptions like Mary got/was married to John. She got/was pregnant and indicated which happened first. Eventuality type of both sentences and reported order were crossed. We find that states tend to be ordered before events, and longer states before shorter states. Our results support a model of discourse comprehension in which eventuality framing is crucial for (temporal) inferences.

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Section: Materials and Proceduresmentioning

confidence: 99%

Eventuality type predicts temporal order inferences in discourse comprehension

Marx,

Wittenberg

2024

Glossa Psycholinguistics

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“…Acceptability exhibits gradience (Keller, 2000;Lau et al, 2014). Accordingly, we treat acceptability as a continuous variable.…”

Section: Introductionmentioning

confidence: 99%

Do We Need Neural Models to Explain Human Judgments of Acceptability?

Wang

Kelly

Reitter

2019

Preprint

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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 Englishlanguage 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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International Speech Communication Association

Furui

2006

Encyclopedia of Language &Amp; Linguistics

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Gradience in Grammar: Experimental and Computational Aspects of Degrees of Grammaticality

Cited by 61 publications

References 75 publications

Eventuality type predicts temporal order inferences in discourse comprehension

Eventuality type predicts temporal order inferences in discourse comprehension

Do We Need Neural Models to Explain Human Judgments of Acceptability?

International Speech Communication Association

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