Hasil untuk "Language acquisition"

George Yule

1. The origins of language 2. Animals and human language 3. The development of writing 4. The sounds of language 5. The sound patterns of language 6. Words and word-formation processes 7. Morphology 8. Phrases and sentences: grammar 9. Syntax 10. Semantics 11. Pragmatics 12. Discourse analysis 13. Language and the brain 14. First language acquisition 15. Second language acquisition/learning 16. Gestures and sign languages 17. Language history and change 18. Language and regional variation 19. Language and social variation 20. Language and culture.

Phil Hiver, Ali H. Al-Hoorie, Joseph P. Vitta et al.

At the turn of the new millennium, in an article published in Language Teaching Research in 2000, Dörnyei and Kormos proposed that ‘active learner engagement is a key concern’ for all instructed language learning. Since then, language engagement research has increased exponentially. In this article, we present a systematic review of 20 years of language engagement research. To ensure robust coverage, we searched 21 major journals on second language acquisition (SLA) and applied linguistics and identified 112 reports satisfying our inclusion criteria. The results of our analysis of these reports highlighted the adoption of heterogeneous methods and conceptual frameworks in the language engagement literature, as well as indicating a need to refine the definitions and operationalizations of engagement in both quantitative and qualitative research. Based on these findings, we attempted to clarify some lingering ambiguity around fundamental definitions, and to more clearly delineate the scope and target of language engagement research. We also discuss future avenues to further advance understanding of the nature, mechanisms, and outcomes resulting from engagement in language learning.

D. Larsen-Freeman

Michael H. Long

D. Larsen-Freeman, Michael H. Long, 蒋 祖康

S. Krashen, T. D. Terrell

K. Wexler, P. Culicover

B. Laufer, J. Hulstijn

R. Wagner, J. Torgesen

Eric Wanner, L. Gleitman

J. Ziegler, U. Goswami

William Nagy, D. Townsend

G. Melzi, Adina R Schick

This chapter examines past research on the role motivation plays in the success of learning a second language. We begin by providing a comprehensive overview of the key conceptual models that have applied the construct of motivation to second language acquisition, namely Gardner and Lambert’s seminal Socio-educational Model of Motivation on Second Language Acquisition. Next, we present an overview of more contemporary conceptual models, which are more inclusive and integrative in nature, and examine how different aspects of the learner and the learning situation might influence motivation and learning outcomes. Then, we turn to the operationalisation and measurement of second language motivation and present an overview of recent empirical work on integrative motivation and second language learning. In our final section, we discuss group differences in motivation and second language acquisition, with an emphasis on the influence of gender, age and culture/ethnicity.

R. Ellis, Natsuko Shintani

S. Bacchini

Marwa Mahmoud Saleh, Eman Talaat Fekry Farag, Maha Hussein Boshnaq et al.

Abstract Background Egyptian cluster substitution has not been targeted in Arabic phonology research. The clusters in the Egyptian language are bi-consonantal and word-final. They have a phonotactic prevalence of /r/ within the two consonants of the cluster. Their final position is also challenging for children during phoneme acquisition. This study adds important structure to the phonological development of Colloquial Egyptian Arabic (CEA) and to the Arabic phonological development in general. The aim is to analyze the substitutional phonological processes of consonant clusters (types and consonant position) used by Egyptian children before cluster acquisition and how they relate to singletons. Methods The study was applied to 150 typically developing (TD) monolingual Arabic Egyptian children, 30 to 48 months. They were divided into three age groups, 6-month interval each. Cluster substitution was assessed using the Egyptian Monosyllabic Consonant Cluster Test (EMCCT). The test contains 50 monosyllabic words commonly used in the Egyptian language, with word-final consonant clusters. Results Devoicing was the commonest cluster substitution process produced by Egyptian children (99.3%), followed by interdental sigmatism (48.7%) and lateralization of /r/ sound (34%). Substitution occurred in both consonants of the cluster but more commonly in the final one (C2). Conclusions Substitution in Egyptian clusters was affected by both place and manner of articulation of the substituted phoneme. It bore resemblance to the substitution of singletons. The final consonant of the cluster (C2) offered a preferred location for substitution. The pattern and position of cluster substitution present a rich addition to the field of Egyptian phonological development.

Badr AlKhamissi, Greta Tuckute, Yingtian Tang et al.

Large language models (LLMs) exhibit remarkable similarity to neural activity in the human language network. However, the key properties of language shaping brain-like representations, and their evolution during training as a function of different tasks remain unclear. We here benchmark 34 training checkpoints spanning 300B tokens across 8 different model sizes to analyze how brain alignment relates to linguistic competence. Specifically, we find that brain alignment tracks the development of formal linguistic competence -- i.e., knowledge of linguistic rules -- more closely than functional linguistic competence. While functional competence, which involves world knowledge and reasoning, continues to develop throughout training, its relationship with brain alignment is weaker, suggesting that the human language network primarily encodes formal linguistic structure rather than broader cognitive functions. We further show that model size is not a reliable predictor of brain alignment when controlling for feature size and find that the correlation between next-word prediction, behavioral alignment and brain alignment fades once models surpass human language proficiency. Finally, using the largest set of rigorous neural language benchmarks to date, we show that language brain alignment benchmarks remain unsaturated, highlighting opportunities for improving future models. Taken together, our findings suggest that the human language network is best modeled by formal, rather than functional, aspects of language.

Zishuo Bao, Yibo Liu, Changyutao Qiu

With the rise of different language model architecture, fine-tuning is becoming even more important for down stream tasks Model gets messy, finding proper hyperparameters for fine-tuning. Although BO has been tried for hyperparameter tuning, most of the existing methods are oblivious to the fact that BO relies on careful choices of acquisition functions, which are essential components of BO that guide how much to explore versus exploit during the optimization process; Different acquisition functions have different levels of sensitivity towards training loss and validation performance; existing methods often just apply an acquisition function no matter if the training and validation performance are sensitive to the acquisition function or not. This work introduces{Bilevel - BO - SWA}, a model fusion approach coupled with a bilevel BO strategy to improve the fine - tunning of large language models. Our work on mixture of acquisition functions like EI and UCB into nested opt loops, where inner loop perform minimization of training loss while outer loops optimized w.r.t. val metric. Experiments on GLUE tasks using RoBERTA - base show that when using EI and UCB, there is an improvement in generalization, and fine - tuning can be improved by up to 2.7%.

Andrew Roxburgh, Floriana Grasso, Terry R. Payne

Predicting the words that a child is going to learn next can be useful for boosting language acquisition, and such predictions have been shown to be possible with both neural network techniques (looking at changes in the vocabulary state over time) and graph model (looking at data pertaining to the relationships between words). However, these models do not fully capture the complexity of the language learning process of an infant when used in isolation. In this paper, we examine how a model of language acquisition for infants and young children can be constructed and adapted for use in a Spatio-Temporal Graph Convolutional Network (STGCN), taking into account the different types of linguistic relationships that occur during child language learning. We introduce a novel approach for predicting child vocabulary acquisition, and evaluate the efficacy of such a model with respect to the different types of linguistic relationships that occur during language acquisition, resulting in insightful observations on model calibration and norm selection. An evaluation of this model found that the mean accuracy of models for predicting new words when using sensorimotor relationships (0.733) and semantic relationships (0.729) were found to be superior to that observed with a 2-layer Feed-forward neural network. Furthermore, the high recall for some relationships suggested that some relationships (e.g. visual) were superior in identifying a larger proportion of relevant words that a child should subsequently learn than others (such as auditory).

Masato Mita, Ryo Yoshida, Yohei Oseki

Large language models possess general linguistic abilities but acquire language less efficiently than humans. This study proposes a method for integrating the developmental characteristics of working memory during the critical period, a stage when human language acquisition is particularly efficient, into the training process of language models. The proposed method introduces a mechanism that initially constrains working memory during the early stages of training and gradually relaxes this constraint in an exponential manner as learning progresses. Targeted syntactic evaluation shows that the proposed method outperforms conventional methods without memory constraints or with static memory constraints. These findings not only provide new directions for designing data-efficient language models but also offer indirect evidence supporting the role of the developmental characteristics of working memory as the underlying mechanism of the critical period in language acquisition.