Output Strictly Local Functions

Chandlee, Jane; Eyraud, Rémi; Heinz, Jeffrey

doi:10.3115/v1/w15-2310

Cited by 26 publications

(50 citation statements)

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“…Chandlee defined three subclasses of subsequential functions-Input Strictly Local (ISL) and Output Strictly Local (OSL, Left-OSL and Right-OSL)-and showed that ISL and OSL functions are sufficient to model a range of local phonological processes such as final voice devoicing, insertion, deletion etc. (Chandlee 2014;Chandlee et al 2014;2015). ISL and OSL functions crosscut the subsequential region, as shown in Figure 1, which suggests that local phonological processes are even less complex than subsequential.…”

Section: Ldca Mappings Are Subsequentialmentioning

confidence: 95%

Long-distance consonant agreement and subsequentiality

Luo

2017

Glossa: A Journal of General Linguistics

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Johnson (1972) and Kaplan & Kay (1994) showed that phonological processes belong to the computational class of regular relations. This paper provides a computational analysis of longdistance consonant agreement and shows that it belongs to a more restricted computational class called subsequential. This paper further argues that subsequentiality is a desirable computational characterization of long-distance consonant agreement for the following reasons. First, it is sufficiently expressive. Second, it is restrictive as it accounts for the absence of pathological patterns like Majority Rules and Sour Grapes from the typology (Heinz & Lai 2013), standing in contrast to Agreement by Correspondence analysis in Optimality Theory (Rose & Walker 2004;Hansson 2007).

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Section: Ldca Mappings Are Subsequentialmentioning

confidence: 95%

Long-distance consonant agreement and subsequentiality

Luo

2017

Glossa: A Journal of General Linguistics

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“…Admittedly, there is not yet a deep understanding of the formal and learnability properties of subregular functions that can model string-to-string mappings. However, recent work has defined a number of relevant classes and identified some of their formal properties (e.g., Chandlee, 2014;Chandlee, Eyraud, & Heinz, 2015;Mohri, 1997), including a number of promising results regarding learnability (Chandlee, Eyraud, & Heinz, 2014;Chandlee & Jardine, 2014). Of particular relevance is the finding that subsequential functions (Mohri, 1997) characterize many attested patterns of longdistance consonant dissimilation (Payne, 2014) and consonant harmony (Luo, 2017), while excluding a number of complex pathologies.…”

Section: Further Issuesmentioning

confidence: 99%

Inductive learning of locality relations in segmental phonology

McMullin

Hansson

2019

Laboratory Phonology: Journal of the Association for Laboratory Phonology

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This paper reports on a series of artificial grammar learning experiments focused on locality relations in patterns of long-distance consonant agreement (harmony) and disagreement (dissimilation). Participants in experimental conditions were exposed to dependencies affecting stem-suffix pairs of liquids at either a short-range (transvocalic, CVCVLV-LV) or medium-range (beyond-transvocalic, CVLVCV-LV) distance. Two experiments used a poverty of stimulus paradigm, offering no information about the other distance level; participants interpreted short-range interaction as a strictly transvocalic dependency but medium-range interaction as unbounded, generalizing to other distances. Two experiments employed a 'rich stimulus' paradigm, where training data unambiguously indicated the absence of any dependency at the other distance; this enabled probing of specific locality patterns, in particular strictly beyond-transvocalic dependencies. The constraint-based Agreement by Correspondence model of non-adjacent consonant interactions predicts such patterns to be possible for dissimilation but not harmony. The results do not support this hypothesis: Participants seem to have serious difficulty learning strictly beyond-transvocalic dependencies of either kind. Our findings are more consistent with recent proposals that the space of learnable phonotactic restrictions is delimited by the Tier-based Strictly 2-Local class of formal languages. Strictly transvocalic and unbounded dependencies lie within this region, whereas strictly beyond-transvocalic dependencies are more complex, falling outside the learner's hypothesis space.Practice CVCV (8) CVCV-li (8) CVCV-ɹu (8) 24Training CVCVLV (96) CVCVlV-li (96) CVCVɹV-li (96) CVCVlV-ɹu (96) CVCVɹV-ɹu (96) 480 CVLVCV (96) CVlVCV-li (96) CVɹVCV-li (96) CVlVCV-ɹu (96) CVɹVCV-ɹu (96) 480 CVCVCV (96) CVCVCV-li (96) CVCVCV-ɹu (96) 288Testing CVCVLV (32) CVCVlV-li (16) CVCVɹV-li (16) CVCVlV-ɹu (16) CVCVɹV-ɹu (16) 96 CVLVCV (32) CVlVCV-li (16) CVɹVCV-li (16) CVlVCV-ɹu (16) CVɹVCV-ɹu (16) 96 LVCVCV (32) lVCVCV-li (16) ɹVCVCV-li (16) lVCVCV-ɹu (16) ɹVCVCV-ɹu (16) 96 Total = 1560

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“…This would open doors to not only better understanding the computational properties of reduplication, but to likewise develop learning algorithms for reduplication. As of now, we hypothesize that a large majority of reduplicative fall under a sub-class of 2-way FSTs (that we have discovered) based on a 2-way extension of the Output-Strictly Local subclass of 1-way FSTs (Chandlee et al, 2015). For more discussion of this subclass for reduplication and its learnability, see Dolatian and Heinz (2018).…”

Section: Resultsmentioning

confidence: 79%

“…Fourth, in the same way that Chandlee 2014; 2017 and Chandlee et al ( , 2015 have studied subclasses of 1-way FSTs and shown how they map to subclasses of morpho-phonology, we are currently investigating what proper subclasses of 2-way FSTs can be designed in order to make a tighter fit with reduplicative typology. This would open doors to not only better understanding the computational properties of reduplication, but to likewise develop learning algorithms for reduplication.…”

Section: Resultsmentioning

confidence: 95%

Modeling Reduplication with 2-way Finite-State Transducers

Dolatian¹,

Heinz²

2018

Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology

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This article describes a novel approach to the computational modeling of reduplication. Reduplication is a well-studied linguistic phenomenon. However, it is often treated as a stumbling block within finite-state treatments of morphology. Most finite-state implementations of computational morphology cannot adequately capture the productivity of unbounded copying in reduplication, nor can they adequately capture bounded copying. We show that an understudied type of finite-state machines, two-way finite-state transducers (2way FSTs), captures virtually all reduplicative processes, including total reduplication. 2-way FSTs can model reduplicative typology in a way which is convenient, easy to design and debug in practice, and linguisticallymotivated. By virtue of being finite-state, 2way FSTs are likewise incorporable into existing finite-state systems and programs. A small but representative typology of reduplicative processes is described in this article, alongside their corresponding 2-way FST models.

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Output Strictly Local Functions

Cited by 26 publications

References 26 publications

Long-distance consonant agreement and subsequentiality

Long-distance consonant agreement and subsequentiality

Inductive learning of locality relations in segmental phonology

Modeling Reduplication with 2-way Finite-State Transducers

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