Empirical Tests of the Gradual Learning Algorithm

By Paul P. G. Boersma, Bruce Hayes. Linguistic Inquiry, 2001

1,083 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Language and Linguistics

View Paper: https://www.semanticscholar.org/paper/3d01d25a2311b8f4ff5ba755bb2adb4fc4f781f8

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Citations in the corpus

BRIDGING THE GAP BETWEEN L2 SPEECH PERCEPTION RESEARCH AND PHONOLOGICAL THEORY (2004, methodology): “…grammar ~e+g+, “74 ms is not 0I0”!, and finally optimize their constraint rankings by applying the Gradual Learning Algorithm ~GLA; Boersma, 1998; Boersma & Hayes, 2001! to their perception grammar, driven by recognition in the lexicon+4 For L1 acquisition, we begin our modeling at the point…”
Analytic bias and phonological typology* (2008, background): “That could happen if HV-non-conformity were more frequent in English than HH- and VV-non-conformity, and the learner incrementally demoted initially high-ranked constraints against each of the three patterns as each non-conforming datum was encountered (Boersma & Hayes 2001).”
The winner takes it all — almost: cumulativity in grammatical variation (2006, background): “(Legendre et al. 2006: 346, emphasis in original) We agree that it is very well possible that strict domination holds for categorical data, while quantitative data display both kinds of (weak) cumulativity.”
Learning Phonology With Substantive Bias: An Experimental and Computational Study of Velar Palatalization (2006, background): “The alternative formulation known as stochastic optimality theory (Boersma, 1998; Boersma & Hayes, 2001 ) is explicitly probabilistic, but the learning algorithm supplied with it has no correctness or convergence proofs and is known to fail to converge in practice (B. Hayes, personal communication,”
Redundancy and reduction: Speakers manage syntactic information density (2010, background): “Bresnan and Hay propose that the animacy effect is grammaticalized as part of gradient grammatical representations (as postulated in, for example, Stochastic Optimality Theory, Boersma & Hayes, 2001).”
The Perceptual Acquisition of Phonological Contrasts (2004, background): “This is an extension of proposals that treat variation as resulting from unranked constraints (see e.g. Anttila 1997, Boersma 1998, Boersma & Hayes 2001, as well as Demuth 1997, Boersma & Levelt 2000, Pater & Werle 2001, and Curtin & Zuraw 2002 on acquisition).”
The linguistic perception of similar L2 sounds (2009, methodology): “Boersma, Escudero & Hayes employ the Gradual Learning Algorithm (GLA, Boersma & Hayes 2001) as the learning device responsible for such development.”
Semantic Density and Past-Tense Formation in Three Germanic Languages (2005, background): “Nevertheless, probability theory has recently found its way into optimality theory ( Boersma & Hayes, 2001 ), and Albright (2002) has documented attractor eects within phonological similarity sets for regular verbs in Italian, contradicting the claims of the dual route model, although he couches his”
Subjects, objects and intonational phrasing in spanish and portuguese* (2005, background): “For a more developed formalisation of the pattern that we have presented here, perhaps a stochastic model of OT should be explored (cf. Boersma 1998, 2001, Hayes 2000, Boersma & Hayes 2001, Pierrehumbert 2001, and references therein), or a theory of weighted constraints (Guy 1991a, b, 1997).”
What Is Optimality Theory? (2007, background): “8 This criterion must be used with care, because of the possibility of variable constraint ranking (Anttila 1997; Boersma and Hayes 2001 and others).”
Morphological effects on the darkness of English intervocalic /l/ (2013, background): “In his strictness band model, a precursor to stochastic Optimality Theory (Boersma and Hayes 2001), constraints can span over a certain range on the ranking scale, allowing overlap with other constraints.”
Weighted constraints and gradient restrictions on place co-occurrence in Muna and Arabic (2008, neutral): “Drawing heavily on proposals of Boersma (1998), Boersma and Hayes (2001) and Zuraw (2000), we show that such weightings emerge as the byproduct of gradual learning.”