How does dialect exposure affect learning to read and spell? An artificial orthography study

Correlational studies have demonstrated detrimental effects of exposure to a mismatch between a non-standard dialect at home and a mainstream variety at school on children’s literacy skills. However, dialect exposure often is confounded with reduced home literacy, negative teacher expectation and more limited educational opportunities. To provide proof of concept for a possible causal relationship between variety mismatch and literacy skills, we taught adult learners to read and spell an artificial language with or without dialect variants using an artificial orthography. In three experiments, we confirmed earlier findings that reading is more error-prone for contrastive words, i.e. words for which different variants exist in the input, especially when learners also acquire the joint meanings of these competing variants. Despite this contrastive deficit, no detriment from variety mismatch emerged for reading and spelling of untrained words, a task equivalent to non-word reading tests routinely administered to young school children. With longer training, we even found a benefit from variety mismatch on reading and spelling of untrained words. We suggest that such a dialect benefit in literacy learning can arise when competition between different variants leads learners to favour phonologically mediated decoding. Our findings should help to assuage educators’ concerns about detrimental effects of linguistic diversity

Exposure to dialect variation in an artificial language prior to literacy training impairs reading of words with competing variants but does not affect decoding skills

Many bidialectal children grow up speaking a variety (e.g. a regional dialect) that differs from the variety in which they subsequently acquire literacy. Previous computational simulations and artificial literacy learning experiments with adults demonstrated lower accuracy in reading contrastive words for which dialect variants exist compared to non-contrastive words without dialect variants. At the same time, exposure to multiple varieties did not affect learners’ ability to phonologically decode untrained words; in fact, longer literacy training resulted in a benefit from dialect exposure as competing variants in the input may have increased reliance on grapheme-phoneme conversion. However, these previous experiments interleaved word learning and reading/spelling training, yet children typically acquire substantial oral language knowledge prior to literacy training. Here we used artificial literacy learning with adults to examine whether the previous findings replicate in an ecologically more valid procedure where word learning precedes literacy training. We also manipulated training conditions to explore interventions thought to be beneficial for literacy acquisition, such as providing explicit social cues for variety use and literacy training in both varieties. Our findings replicated the reduced accuracy for reading contrastive words in those learners who had successfully acquired the dialect variants prior to literacy training. This effect was exacerbated when literacy training also included dialect variation. Crucially, although no benefits from the interventions were found, dialect exposure did not affect reading and spelling of untrained words suggesting that phonological decoding skills can remain unaffected by the existence of multiple word form variants in a learner’s lexicon

Language transfer and positional bias in English stress

This paper shows that L1 transfer may not be effectively maintained in the interlanguage due to confounding factors in the L2. When two factors, A and B, are correlated in the L2, second language learners may only acquire B, even if A is present in the L1. Transfer may not be effective because B, being more robust in the input, conceals A. Native speakers, on the other hand, generalize A in spite of B. The variables in question are weight-sensitivity (A) and positional bias (B) in English, both of which can predict the location of stress in the language. I show that two seemingly target-like groups of second language learners of English (speakers of Mandarin and speakers Portuguese) fail to accurately generalize weight-sensitivity in the language, and instead display response patterns which are predictable given the existing positional bias in English stress

Unsupervised Lexicon Discovery from Acoustic Input

We present a model of unsupervised phonological lexicon discovery -- the problem of simultaneously learning phoneme-like and word-like units from acoustic input. Our model builds on earlier models of unsupervised phone-like unit discovery from acoustic data (Lee and Glass, 2012), and unsupervised symbolic lexicon discovery using the Adaptor Grammar framework (Johnson et al., 2006), integrating these earlier approaches using a probabilistic model of phonological variation. We show that the model is competitive with state-of-the-art spoken term discovery systems, and present analyses exploring the model's behavior and the kinds of linguistic structures it learns

Statistical Knowledge and Learning in Phonology

This thesis deals with the theory of the phonetic component of grammar in a formal probabilistic inference framework: (1) it has been recognized since the beginning of generative phonology that some language-specific phonetic implementation is actually context-dependent, and thus it can be said that there are gradient "phonetic processes" in grammar in addition to categorical "phonological processes." However, no explicit theory has been developed to characterize these processes. Meanwhile, (2) it is understood that language acquisition and perception are both really informed guesswork: the result of both types of inference can be reasonably thought to be a less-than-perfect committment, with multiple candidate grammars or parses considered and each associated with some degree of credence. Previous research has used probability theory to formalize these inferences in implemented computational models, especially in phonetics and phonology. In this role, computational models serve to demonstrate the existence of working learning/per- ception/parsing systems assuming a faithful implementation of one particular theory of human language, and are not intended to adjudicate whether that theory is correct. The current thesis (1) develops a theory of the phonetic component of grammar and how it relates to the greater phonological system and (2) uses a formal Bayesian treatment of learning to evaluate this theory of the phonological architecture and for making predictions about how the resulting grammars will be organized. The coarse description of the consequence for linguistic theory is that the processes we think of as "allophonic" are actually language-specific, gradient phonetic processes, assigned to the phonetic component of grammar; strict allophones have no representation in the output of the categorical phonological grammar

Extending adaptor grammars to learn phonological alternations

Recent advances in unsupervised learning of linguistic structure have demonstrated the feasibility of inferring latent morphological parses from an unannotated corpus given transparent underlying-to-surface mappings (ex., Adaptor Grammars), as well as in learning predictable phonological transformations from prespecified underlying morphemes to a range of surface allomorphs via a stochastic edit distance algorithm. In this paper we introduce a nonparametric Bayesian model which builds on the morpheme-segmentation success of AGs, and incorporates the ability to learn predictable phonological transformations of underlying forms to their surface allomorphs via the interaction of markedness and faithfulness principles, inspired by generative phonology. The unsupervised nature of this model (that is, no semantic information about the words being segmented is provided) is relevant not only computationally but also psychologically, as it mirrors developmental findings that young infants segment and cluster morphemes based solely on phonetic and distributional similarity. The model also incorporates many of the other cognitive restrictions infants during the initial period of morphophonological learning in an effort to make the model maximally realistic, and thus eventually useful in making quantitative predictions about the early stages of morphophonological acquisition that can be experimentally investigated. We evaluate the model on a novel dataset consisting of a complex system of allomorphy in Acehnese, an understudied Indonesian language

Input and Intake in Language Acquisition

This dissertation presents an approach for a productive way forward in the study of language acquisition, sealing the rift between claims of an innate linguistic hypothesis space and powerful domain general statistical inference. This approach breaks language acquisition into its component parts, distinguishing the input in the environment from the intake encoded by the learner, and looking at how a statistical inference mechanism, coupled with a well defined linguistic hypothesis space could lead a learn to infer the native grammar of their native language. This work draws on experimental work, corpus analyses and computational models of Tsez, Norwegian and English children acquiring word meanings, word classes and syntax to highlight the need for an appropriate encoding of the linguistic input in order to solve any given problem in language acquisition