The Complex Role of Utterance Length on Grammaticality: Multivariate Multilevel Analysis of English and Spanish Utterances of First-Grade English Learners

Castilla-Earls, Anny; Francis, D.; Iglesias, Aquiles

doi:10.1044/2021_jslhr-20-00464

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…While this is a reasonable first step for the study of grammaticality, many patterns are dependent on specific error types. For example, the effects of utterance length on grammaticality differ for errors of omission vs. commission (Castilla-Earls et al, 2022). Further, Saxton et al (2005a) found that corrective feedback for syntactic errors is more frequent than for morphological errors, and that negative feedback in the form of reformulations is associated with gains in the grammaticality of child speech for 3 out of 13 tested grammatical error categories.…”

Section: Limitationsmentioning

confidence: 99%

Automatic Annotation of Grammaticality in Child-Caregiver Conversations

Nikolaus,

Agrawal,

Kaklamanis

et al. 2024

Preprint

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The acquisition of grammar has been a central question to adjudicate between theories of language acquisition. In order to conduct faster, more reproducible, and larger-scale corpus studies on grammaticality in child-caregiver conversations, tools for automatic annotation can offer an effective alternative to tedious manual annotation. We propose a coding scheme for context-dependent grammaticality in child-caregiver conversations and annotate more than 4,000 utterances from a large corpus of transcribed conversations. Based on these annotations, we train and evaluate a range of NLP models. Our results show that fine-tuned Transformer-based models perform best, achieving human inter-annotation agreement levels. As a first application and sanity check of this tool, we use the trained models to annotate a corpus that is almost two orders of magnitude larger than the manually annotated data and verify that children's grammaticality shows a steady increase with age. This work contributes to the growing literature on applying state-of-the-art NLP methods to help study child language acquisition at scale.

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Section: Limitationsmentioning

confidence: 99%

Automatic Annotation of Grammaticality in Child-Caregiver Conversations

Nikolaus,

Agrawal,

Kaklamanis

et al. 2024

Preprint

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Investigating Narrative Performance in Children With Developmental Language Disorder: A Systematic Review and Meta-Analysis

Winters

Jasso

Pustejovsky

et al. 2022

J Speech Lang Hear Res

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Purpose: Narrative assessment is one potentially underutilized and inconsistent method speech-language pathologists may use when considering a diagnosis of developmental language disorder (DLD). However, narration research encompasses many varied methodologies. This systematic review and meta-analysis aimed to (a) investigate how various narrative assessment types (e.g., macrostructure, microstructure, and internal state language) differentiate children with typical development (TD) from children with DLD, (b) identify specific narrative assessment measures that result in greater group differences, and (c) evaluate participant and sample characteristics that may influence performance differences. Method: Electronic databases (PsycINFO, ERIC, and PubMed) and ASHAWire were searched on July 30, 2019, to locate studies that reported oral narrative language measures for both DLD and TD groups between ages 4 and 12 years; studies focusing on written narration or other developmental disorders only were excluded. We extracted data related to sample participants, narrative task(s) and assessment measures, and research design. Group differences were quantified using standardized mean differences. Analyses used mixed-effects meta-regression with robust variance estimation to account for effect size dependencies. Results: Searches identified 37 eligible studies published between 1987 and 2019, including 382 effect sizes. Overall meta-analysis showed that children with DLD had decreased narrative performance relative to TD peers, with an overall average effect of −0.82 SD , 95% confidence interval [−0.99, −0.66]. Effect sizes showed significant heterogeneity both between and within studies, even after accounting for effect size–, sample-, and study-level predictors. Across model specifications, grammatical accuracy (microstructure) and story grammar (macrostructure) yielded the most consistent evidence of TD–DLD group differences. Conclusions: Present findings suggest some narrative assessment measures yield significantly different performance between children with and without DLD. However, researchers need to improve consistency of inclusionary criteria, descriptions of sample characteristics, and reporting of correlations between measures to determine which assessment measures reliably distinguish between groups. Supplemental Material: https://doi.org/10.23641/asha.21200380

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Diagnostic Accuracy of Grammaticality and Utterance Length in Bilingual Children

Hernandez,

Ronderos,

Castilla-Earls

2024

LSHSS

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Purpose: The purpose of this study was to investigate the diagnostic accuracy of two measures derived from spontaneous language samples, mean length of utterance in words (MLUw) and percentage of grammatical utterances (PGU), in identifying developmental language disorder (DLD) in Spanish–English bilingual children. We examined two approaches: best language and total language. Method: The participants in this study included 74 Spanish–English bilingual children with ( n = 36) and without ( n = 38) DLD. Language samples were elicited through a story retell and story generation task using Frog wordless picture books in English and Spanish. Stories were transcribed and coded using the Systematic Analysis of Language Samples (Miller & Iglesias, 2020) to extract MLUw and PGU in both languages. Results: Logistic regression analyses suggested that a model that included PGU, MLUw, and age achieved the best diagnostic accuracy in predicting group membership. Both approaches, best language and total language, had fair diagnostic accuracy. Conclusions: In combination, PGU and MLUw seem to be useful diagnostic tools to differentiate bilingual children with and without DLD. Clinical implications and usability are discussed.

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The Complex Role of Utterance Length on Grammaticality: Multivariate Multilevel Analysis of English and Spanish Utterances of First-Grade English Learners

Cited by 3 publications

References 30 publications

Automatic Annotation of Grammaticality in Child-Caregiver Conversations

Automatic Annotation of Grammaticality in Child-Caregiver Conversations

Investigating Narrative Performance in Children With Developmental Language Disorder: A Systematic Review and Meta-Analysis

Diagnostic Accuracy of Grammaticality and Utterance Length in Bilingual Children

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