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The effect of three basic task features on the sensitivity of acceptability judgment tasks
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In: Glossa: a journal of general linguistics (2016-2021) ; https://hal.archives-ouvertes.fr/hal-03101517 ; Glossa: a journal of general linguistics (2016-2021), Ubiquity Press, 2020, 5 (1), pp.72. ⟨10.5334/gjgl.980⟩ (2020)
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COMPARING SOLUTIONS TO THE LINKING PROBLEM USING AN INTEGRATED QUANTITATIVE FRAMEWORK OF LANGUAGE ACQUISITION
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In: LANGUAGE, vol 95, iss 4 (2019)
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Colorless green ideas do sleep furiously: gradient acceptability and the nature of the grammar
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In: De Gruyter (2019)
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Abstract:
In their recent paper, Lau, Clark, and Lappin explore the idea that the probability of the occurrence of word strings can form the basis of an adequate theory of grammar (Lau, Jey H., Alexander Clark & 15 Shalom Lappin. 2017. Grammaticality, acceptability, and probability: A prob- abilistic view of linguistic knowledge. Cognitive Science 41(5):1201-1241). To make their case, they present the results of correlating the output of several probabilistic models trained solely on naturally occurring sentences with the gradient acceptability judgments that humans report for ungrammatical sentences derived from roundtrip machine translation errors. In this paper, we first explore the logic of the Lau et al. argument, both in terms of the choice of evaluation metric (gradient acceptability), and in the choice of test data set (machine translation errors on random sentences from a corpus). We then present our own series of studies intended to allow for a better comparison between LCL's models and existing grammatical theories. We evaluate two of LCL's probabilistic models (trigrams and recurrent neural network) against three data sets (taken from journal articles, a textbook, and Chomsky's famous colorless-green-ideas sentence), using three evaluation metrics (LCL's gradience metric, a categorical version of the metric, and the experimental-logic metric used in the syntax literature). Our results suggest there are very real, measurable cost-benefit tradeoffs inherent in LCL's models across the three evaluation metrics. The gain in explanation of gradience (between 13% and 31% of gradience) is offset by losses in the other two metrics: a 43%-49% loss in coverage based on a categorical metric of explaining acceptability, and a loss of 12%-35% in explaining experimentally-defined phenomena. This suggests that anyone wishing to pursue LCL's models as competitors with existing syntactic theories must either be satisfied with this tradeoff, or modify the models to capture the phenomena that are not currently captured.
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URL: https://hdl.handle.net/1721.1/125923
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Rhetorical questions as questions
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In: Sinn und Bedeutung; Bd. 11 (2007): Proceedings of Sinn und Bedeutung 11; 121-133 ; Proceedings of Sinn und Bedeutung; Vol 11 (2007): Proceedings of Sinn und Bedeutung 11; 121-133 ; 2629-6055 (2019)
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Grammar and the use of data
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In: Sprouse, Jon; & Schütze, Carson T. (2017). Grammar and the use of data. In The Oxford Handbook of English Grammar Location: Oxford University Press. UCLA: Retrieved from: http://www.escholarship.org/uc/item/0n100842 (2017)
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| 7 |
Design sensitivity and statistical power in acceptability judgment experiments
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In: Glossa: a journal of general linguistics; Vol 2, No 1 (2017); 14 ; 2397-1835 (2017)
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Investigating Variation in Island Effects: A Case Study of Norwegian Wh-Extraction
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Experimental syntax and island effects
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Sprouse, Jon. - Cambridge [u.a.] : Cambridge Univ. Pr., 2013
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MPI-SHH Linguistik
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| 18 |
A comparison of informal and formal acceptability judgments using a random sample from Linguistic Inquiry 2001-2010
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In: LINGUA, vol 134 (2013)
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Syntactic Islands and Learning Biases: Combining Experimental Syntax and Computational Modeling to Investigate the Language Acquisition Problem
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In: Pearl, Lisa; & Sprouse, Jon. (2013). Syntactic Islands and Learning Biases: Combining Experimental Syntax and Computational Modeling to Investigate the Language Acquisition Problem. Language Acquisition, 20(1), 23 - 68. doi:10.1080/10489223.2012.738742. UC Irvine: Retrieved from: http://www.escholarship.org/uc/item/1tf6r4cf (2013)
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