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Representing Multiple Dependencies in Prosodic Structures
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 2 |
Learnability of indexed constraint analyses of phonological opacity
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In: Proceedings of the Society for Computation in Linguistics (2021)
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| 3 |
LEARNING PHONOLOGY WITH SEQUENCE-TO-SEQUENCE NEURAL NETWORKS
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In: Doctoral Dissertations (2021)
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| 4 |
Emergent Typological Effects of Agent-Based Learning Models in Maximum Entropy Grammar
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In: Doctoral Dissertations (2020)
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Abstract:
This dissertation shows how a theory of grammatical representations and a theory of learning can be combined to generate gradient typological predictions in phonology, predicting not only which patterns are expected to exist, but also their relative frequencies: patterns which are learned more easily are predicted to be more typologically frequent than those which are more difficult. In Chapter 1 I motivate and describe the specific implementation of this methodology in this dissertation. Maximum Entropy grammar (Goldwater & Johnson 2003) is combined with two agent-based learning models, the iterated and the interactive learning model, each of which mimics a type of learning dynamic observed in natural language acquisition. In Chapter 2 I illustrate how this system works using a simplified, abstract example typology, and show how the models generate a bias away from patterns which rely on cumulative constraint interaction ("gang effects"), and a bias away from variable patterns. Both of these biases match observed trends in natural language typology and psycholinguistic experiments. Chapter 3 further explores the models' bias away from cumulative constraint interaction using an empirical test case: the typology of possible patterns of contrast between two fricatives. This typology yields five possible patterns, the rarest of which is the result of a gang effect. The results of simulations performed with both models produce a bias against the gang effect pattern. Chapter 4 further explores the models' bias away from variation using evidence from artificial grammar learning experiments, in which human participants show a bias away from variable patterns (e.g. Smith & Wonnacott 2010). This test case was chosen additionally to disambiguate between variable behavior within a lexical item (variation), and variable behavior across lexical items (exceptionality). The results of simulations performed with both learning models are consistent with the observed bias away from variable patterns in humans. The results of the iterated and interactive learning models presented in this dissertation provide support for the use of this methodology in investigating the typological predictions of linguistic theories of grammar and learning, as well as in addressing broader questions regarding the source of gradient typological trends, and whether certain properties of natural language must be innately specified, or might emerge through other means.
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Keyword:
Computational Linguistics; linguistics; Phonetics and Phonology; phonology; typology
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URL: https://scholarworks.umass.edu/dissertations_2/2028
https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=3111&context=dissertations_2
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| 5 |
The stability of segmental properties across genre and corpus types in low-resource languages
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In: Proceedings of the Society for Computation in Linguistics (2020)
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| 6 |
Multi-Input Strict Local Functions for Tonal Phonology
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In: Proceedings of the Society for Computation in Linguistics (2020)
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| 7 |
Q-Theory Representations are Logically Equivalent to Autosegmental Representations
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In: Proceedings of the Society for Computation in Linguistics (2019)
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| 8 |
Phonological opacity as local optimization in Gradient Symbolic Computation
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In: Proceedings of the Society for Computation in Linguistics (2018)
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| 9 |
Similarity-based Phonological Generalization
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In: Proceedings of the Society for Computation in Linguistics (2018)
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| 10 |
Formal Restrictions On Multiple Tiers
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In: Proceedings of the Society for Computation in Linguistics (2018)
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| 11 |
Investigating the Consequences of Iterated Learning in Phonological Typology
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In: Proceedings of the Society for Computation in Linguistics (2018)
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| 12 |
Computational Modeling of Learning Biases in Stress Typology
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In: Doctoral Dissertations (2014)
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