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Neural pathways of phonological and semantic processing and its relations to children’s reading skills ...
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Do we recognize whether a man's masculinity is threatened? An auditory perception experiment ...
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Word form generalisation across voices: the role of infant sleep. ...
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F0 and syllable lengthening as correlates to stress in Spanish segmentation ...
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How biased are listeners towards second language speech? A replication and extension ...
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A split-gesture, competitive, coupled oscillator model of syllable structure predicts the emergence of edge gemination and degemination
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In: Proceedings of the Society for Computation in Linguistics (2022)
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Linguistic Complexity and Planning Effects on Word Duration in Hindi Read Aloud Speech
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In: Proceedings of the Society for Computation in Linguistics (2022)
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MaxEnt Learners are Biased Against Giving Probability to Harmonically Bounded Candidates
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In: Proceedings of the Society for Computation in Linguistics (2022)
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Abstract:
One of the major differences between MaxEnt Harmonic Grammar (Goldwater and Johnson, 2003) and Noisy Harmonic Grammar (Boersma and Pater, 2016) is that in MaxEnt harmonically bounded candidates are able to get some probability, whereas in most other constraint-based grammars they can never be output (Jesney, 2007). The probability given to harmonically bounded candidates is taken from other candidates, in some cases allowing Max- Ent to model grammars that subvert some of the universal implications that are true in NoisyHG (Anttila and Magri, 2018). Magri (2018) argues that the types of implicational universals that remain valid in MaxEnt are phonologically implausible, suggesting that Max- Ent overgenerates NoisyHG. However, recent work has shown that some of the possible grammars in a constraint based grammar may be unlikely to be observed because they are difficult to learn (Staubs, 2014; Stanton, 2016; Pater and Moreton, 2012; Hughto, 2019; O’Hara, 2021). Here, I show that grammars that give weight to harmonically bounded candidates are harder to learn than other grammars. With learnability applied, I claim that the typological predictions of MaxEnt and NoisyHG are in fact much more similar than they would seem based on the grammars alone.
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Keyword:
Computational Linguistics; computational phonology; harmonically bounded; learning bias; MaxEnt; noisy harmonic grammar; Phonetics and Phonology; phonological learning; Typological Linguistics and Linguistic Diversity; typological overgeneration
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URL: https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1252&context=scil
https://scholarworks.umass.edu/scil/vol5/iss1/24
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| 14 |
Representing Multiple Dependencies in Prosodic Structures
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In: Proceedings of the Society for Computation in Linguistics (2022)
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La pronunciación del latín en la América dieciochesca: el caso de la Audiencia de Guatemala ...
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La pronunciación del latín en la América dieciochesca: el caso de la Audiencia de Guatemala ...
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