Efficient learning of Output Tier-based Strictly 2-Local functions

Burness, Phillip; McMullin, Kevin

doi:10.18653/v1/w19-5707

Cited by 5 publications

(9 citation statements)

References 14 publications

(25 reference statements)

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“…Finally, future work will consider the learnability of MTSL functions. Burness and McMullin (2019) showed that any OTSL function is efficiently learnable from positive data if the tier is known in advance. This result can likely be carried over to MTSL functions once they receive a suitable automata characterization.…”

Section: Discussionmentioning

confidence: 99%

“…As discussed in Burness and McMullin (2019), the TSL functions are quite versatile, being able to model long-distance harmony and long-distance dissimilation, both with and without blocking effects. This is, however, only the case when we model each phonological process of a language in isolation.…”

Section: Limitations Of Tsl Functionsmentioning

confidence: 99%

“…Within the subregular hierarchy of formal languages, the class of Strictly Local languages, which model local phonotactic restrictions (Mc-Naughton and Papert, 1971;Rogers and Pullum, 2011;Rogers et al, 2013) have been extended to incorporate the notion of a tier, resulting in the class of Tier-based Strictly Local (TSL) languages (Heinz et al, 2011). Likewise, the Input Strictly Local (ISL) and Output Strictly Local (OSL) functions-which characterize locallybounded phonological processes as subregular maps (Chandlee, 2014;Chandlee et al, 2014Chandlee et al, , 2015-have been generalized to the classes of Input Tier-based Strictly Local (ITSL) and Output Tier-based Strictly Local (OTSL) functions in order to account for non-local phonological processes (Burness and McMullin, 2019;Hao and Andersson, 2019;Hao and Bowers, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…These TSL formal languages and functions successfully model a wide range of long-distance phonological patterns (McMullin, 2016;Mc-Mullin and Hansson, 2016;Burness and Mc-Mullin, 2019;Hao and Bowers, 2019), and moreover have desirable properties for learnability (Jardine and Heinz, 2016;Jardine and McMullin, 2017;Burness and McMullin, 2019). However, they suffer from a major drawback in that they are restricted to a single tier, and as a consequence are ill-equipped to deal with multiple, simultaneous long-distance dependencies.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Tiered Strictly Local Functions

Burness¹,

McMullin²

2020

Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

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Tier-based Strictly Local functions, as they have so far been defined, are equipped with just a single tier. In light of this fact, they are currently incapable of modelling simultaneous phonological processes that would require different tiers. In this paper we consider whether and how we can allow a single function to operate over more than one tier. We conclude that multiple tiers can and should be permitted, but that the relationships between them must be restricted in some way to avoid overgeneration. The particular restriction that we propose comes in two parts. First, each input element is associated with a set of tiers that on their own can fully determine what the element is mapped to. Second, the set of tiers associated to a given input element must form a strict superset-subset hierarchy. In this way, we can track multiple, related sources of information when deciding how to process a particular input element. We demonstrate that doing so enables simple and intuitive analyses to otherwise challenging phonological phenomena.

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Section: Discussionmentioning

confidence: 99%

Section: Limitations Of Tsl Functionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Tiered Strictly Local Functions

Burness¹,

McMullin²

2020

Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

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“…Previous research has suggested that learnability of non-adjacent dependencies in phonology may require parsing vowels and consonants on different tiers (Burness & McMullin, 2019;Chandlee, 2014;Heinz, 2010;McMullin, 2016;Newport & Aslin, 2004), and that learners show biases against root and pattern morphology with roots that contain a mix of consonants and vowels (Fullwood, 2018).…”

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confidence: 99%

Non-Concatenative Morpheme Segmentation in Adults and Children

Finley¹,

Newport²

2021

Preprint

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While most morphemes in the world’s language involve continuous structure or concatenation (e.g., prefixes and suffixes), many languages show some form of non-adjacent, non-concatenative morphology. Non-concatenative morphology poses a challenge for statistical learning approaches to morpheme segmentation because the combinatorial possibilities greatly increase for non-adjacent dependencies. The present study explores the types of dependencies that human learners (school-aged children and adults) are able to extract from exposure to a miniature, artificial non-concatenative system. In Experiment 1, participants were exposed to 12 CCC ‘roots’ that fit into 72 CVCVC skeletons with a high variety of VV ‘residue’. Experiment 2 extended Experiment 1 to school-aged children (with adult controls). Experiment 3 replicated Experiment 1, but with ‘mixed’ consonant-vowel roots and residues. Across all three experiments, participants were able to recognize familiar items compared to novel items, but had limited ability to generalize the CCC roots to novel items, suggesting a limited ability to parse consonantal roots. Adults were better at generalizing to novel items compared to children.

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Diving deeper into subregular syntax

Graf

2022

Theoretical Linguistics

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Efficient learning of Output Tier-based Strictly 2-Local functions

Cited by 5 publications

References 14 publications

Multi-Tiered Strictly Local Functions

Multi-Tiered Strictly Local Functions

Non-Concatenative Morpheme Segmentation in Adults and Children

Diving deeper into subregular syntax

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