Gaja Jarosz
Biographic Data
| ID | 3432999 |
|---|---|
| NAME | Gaja Jarosz |
| GIVEN NAMES | Gaja |
| FAMILY NAME | Jarosz |
| SIGNATURE | JAROSZ G |
| AFFILIATIONS | University of Massachusetts Amherst |
| ORCID | 0000-0002-8606-4704 |
| VERIFIED | Yes |
| TOTAL WORKS | 14 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 14 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2010 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Incremental learning of lexically specific morphophonology
Acquisition and processing results indicate idiosyncratic, lexical knowledge interacts with productive, grammatical knowledge in systematic ways. Evidence from language processing demonstrates that higher frequency and less productive complex words are more likely to be retrieved holistically from the mental lexicon rather than being decomposed into constituents. Meanwhile, acquisition findings in both natural and artificial languages provide imp…
Type and token frequency jointly drive learning of morphology
The Credit Problem in parametric stress
In this paper, we introduce a novel domain-general, statistical learning model for P&P grammars: the Expectation Driven Parameter Learner (EDPL). We show that the EDPL provides a mathematically principled solution to the Credit Problem (Dresher 1999). We present the first systematic tests of the EDPL and an existing and closely related model, the Naïve Parameter Learner (NPL), on a full stress typology, the one generated by Dresher & Kaye’s (1990…
Modeling the Acquisition of Phonological Interactions
In this paper we computationally implement four different theories for representing opaque and transparent phonological interactions: Harmonic Serialism, Stratal OT, Two-Level Constraints, and Indexed Constraints. We then show that these theories make unique predictions on two tasks: (1) a learning-bias task, based on previous experimental work with humans and (2) a novel generalization task that no human data exists for. Our results in (1) show …
Computational Modeling of Phonological Learning
Recent advances in computational modeling have led to significant discoveries about the representation and acquisition of phonological knowledge and the limits on language learning and variation. These discoveries are the result of applying computational learning models to increasingly rich and complex natural language data while making increasingly realistic assumptions about the learning task. This article reviews the recent developments in com…
Defying the stimulus
Behavioural findings indicate that English, Mandarin and Korean speakers exhibit gradient sonority sequencing preferences among unattested initial clusters. While some have argued these results support an innate principle, recent modelling studies have questioned this conclusion, showing that computational models capable of inducing generalisations using abstract phonological features can detect these preferences from lexical statistics in the th…
Sonority Sequencing in Polish
A growing body of behavioral results demonstrates cross-linguistic sensitivity to the SSP (Daland et al. 2011; Berent et al. 2007; Berent et al. 2008; Jarosz to appear; Ren, Gao & Morgan 2010). These consistent findings suggest a role for prior bias in phonological learning, but recent modeling studies question this conclusion, showing that for some languages these preferences can be derived from the input (Daland et al. 2011; Hayes 2011). Buildi…
Learning Parametric Stress without Domain-Specific Mechanisms
State-of-the-art learning mechanisms for stress in Optimality Theory (see, e.g., Tesar and Smolensky 2000; Boersma and Pater 2016; Jarosz 2013) make use of probabilistic mechanisms that are domain-general in that they do not refer to the content of constraints and must not be in UG. By contrast, Pearl (2007, 2011) has argued that domain-general probabilistic learners of parametric grammars (Yang 2002) are insufficient for word stress, and, instea…
Learning Opaque and Transparent Interactions in Harmonic Serialism
This paper presents initial modeling results comparing the relative learnability of four basic types of process interactions: bleeding, feeding, counterfeeding and counterbleeding. The principle finding is that the learning model does not predict an absolute hierarchy of learning difficulty based on the type of process interaction involved. Rather, relative learning difficulty depends on numerous interacting factors, including how evidence for th…
Serial Markedness Reduction
A growing body of research provides evidence supporting Harmonic Serialism (HS; McCarthy 2000, 2008a, 2010; Elfner to appear; Pruitt 2010), a serial version of Optimality Theory in which a single ranking of constraints is used iteratively to construct a derivation. This work has led to new insights into several outstanding problems in phonology and has highlighted problematic over-predictions of parallel OT, showing that HS predicts a more restri…
Polish Yers and the Finer Structure of Output-Output Correspondence
The fundamental problem posed by Slavic alternating vowels, called yers, is represented by the three nominal paradigms in (1). In (1b) a full vowel appears throughout the inflectional paradigm; the stem is invariant. In (1c) the stem is also invariant and ends in a cluster regardless of the following inflection. The paradigm
Polish Yers and the Finer Structure of Output-Output Correspondence
Learning with hidden structure in Optimality Theory and Harmonic Grammar
This paper explores the relative merits of constraint ranking vs . weighting in the context of a major outstanding learnability problem in phonology: learning in the face of hidden structure. Specifically, the paper examines a well-known approach to the structural ambiguity problem, Robust Interpretive Parsing (RIP; Tesar & Smolensky 1998), focusing on its stochastic extension first described by Boersma (2003). Two related problems with the stoch…
Implicational markedness and frequency in constraint-based computational models of phonological learning
This study examines the interacting roles of implicational markedness and frequency from the joint perspectives of formal linguistic theory, phonological acquisition and computational modeling. The hypothesis that child grammars are rankings of universal constraints, as in Optimality Theory (Prince & Smolensky, 1993/2004), that learning involves a gradual transition from an unmarked initial state to the target grammar, and that order of acquisiti…
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Implicational markedness and frequency in constraint-based computational models of phonological learning
This study examines the interacting roles of implicational markedness and frequency from the joint perspectives of formal linguistic theory, phonological acquisition and computational modeling. The hypothesis that child grammars are rankings of universal constraints, as in Optimality Theory (Prince & Smolensky, 1993/2004), that learning involves a gradual transition from an unmarked initial state to the target grammar, and that order of acquisiti…
Learning with hidden structure in Optimality Theory and Harmonic Grammar
This paper explores the relative merits of constraint ranking vs . weighting in the context of a major outstanding learnability problem in phonology: learning in the face of hidden structure. Specifically, the paper examines a well-known approach to the structural ambiguity problem, Robust Interpretive Parsing (RIP; Tesar & Smolensky 1998), focusing on its stochastic extension first described by Boersma (2003). Two related problems with the stoch…
Serial Markedness Reduction
A growing body of research provides evidence supporting Harmonic Serialism (HS; McCarthy 2000, 2008a, 2010; Elfner to appear; Pruitt 2010), a serial version of Optimality Theory in which a single ranking of constraints is used iteratively to construct a derivation. This work has led to new insights into several outstanding problems in phonology and has highlighted problematic over-predictions of parallel OT, showing that HS predicts a more restri…
Polish Yers and the Finer Structure of Output-Output Correspondence
The fundamental problem posed by Slavic alternating vowels, called yers, is represented by the three nominal paradigms in (1). In (1b) a full vowel appears throughout the inflectional paradigm; the stem is invariant. In (1c) the stem is also invariant and ends in a cluster regardless of the following inflection. The paradigm
Polish Yers and the Finer Structure of Output-Output Correspondence
Learning Opaque and Transparent Interactions in Harmonic Serialism
This paper presents initial modeling results comparing the relative learnability of four basic types of process interactions: bleeding, feeding, counterfeeding and counterbleeding. The principle finding is that the learning model does not predict an absolute hierarchy of learning difficulty based on the type of process interaction involved. Rather, relative learning difficulty depends on numerous interacting factors, including how evidence for th…
Defying the stimulus
Behavioural findings indicate that English, Mandarin and Korean speakers exhibit gradient sonority sequencing preferences among unattested initial clusters. While some have argued these results support an innate principle, recent modelling studies have questioned this conclusion, showing that computational models capable of inducing generalisations using abstract phonological features can detect these preferences from lexical statistics in the th…
Sonority Sequencing in Polish
A growing body of behavioral results demonstrates cross-linguistic sensitivity to the SSP (Daland et al. 2011; Berent et al. 2007; Berent et al. 2008; Jarosz to appear; Ren, Gao & Morgan 2010). These consistent findings suggest a role for prior bias in phonological learning, but recent modeling studies question this conclusion, showing that for some languages these preferences can be derived from the input (Daland et al. 2011; Hayes 2011). Buildi…
Learning Parametric Stress without Domain-Specific Mechanisms
State-of-the-art learning mechanisms for stress in Optimality Theory (see, e.g., Tesar and Smolensky 2000; Boersma and Pater 2016; Jarosz 2013) make use of probabilistic mechanisms that are domain-general in that they do not refer to the content of constraints and must not be in UG. By contrast, Pearl (2007, 2011) has argued that domain-general probabilistic learners of parametric grammars (Yang 2002) are insufficient for word stress, and, instea…
Computational Modeling of Phonological Learning
Recent advances in computational modeling have led to significant discoveries about the representation and acquisition of phonological knowledge and the limits on language learning and variation. These discoveries are the result of applying computational learning models to increasingly rich and complex natural language data while making increasingly realistic assumptions about the learning task. This article reviews the recent developments in com…
The Credit Problem in parametric stress
In this paper, we introduce a novel domain-general, statistical learning model for P&P grammars: the Expectation Driven Parameter Learner (EDPL). We show that the EDPL provides a mathematically principled solution to the Credit Problem (Dresher 1999). We present the first systematic tests of the EDPL and an existing and closely related model, the Naïve Parameter Learner (NPL), on a full stress typology, the one generated by Dresher & Kaye’s (1990…
Modeling the Acquisition of Phonological Interactions
In this paper we computationally implement four different theories for representing opaque and transparent phonological interactions: Harmonic Serialism, Stratal OT, Two-Level Constraints, and Indexed Constraints. We then show that these theories make unique predictions on two tasks: (1) a learning-bias task, based on previous experimental work with humans and (2) a novel generalization task that no human data exists for. Our results in (1) show …
Incremental learning of lexically specific morphophonology
Acquisition and processing results indicate idiosyncratic, lexical knowledge interacts with productive, grammatical knowledge in systematic ways. Evidence from language processing demonstrates that higher frequency and less productive complex words are more likely to be retrieved holistically from the mental lexicon rather than being decomposed into constituents. Meanwhile, acquisition findings in both natural and artificial languages provide imp…
Type and token frequency jointly drive learning of morphology
Computer Science (10 works) · Phonetics and Phonology Research (9 works) · Artificial Intelligence (8 works) · Linguistics (8 works) · Mathematics (8 works) · Natural language processing (8 works) · Natural Language Processing Techniques (7 works) · Phonology (6 works) · Speech and dialogue systems (6 works) · Optimality theory (5 works)