Two Distinct Sequence Learning Mechanisms for Syntax Acquisition and Word Learning

2013 ◽  
pp. 350-369
Author(s):  
Anne McClure Walk ◽  
Christopher M. Conway
Keyword(s):  
Language Learning ◽  
Language Processing ◽  
Word Learning ◽  
Sequence Learning ◽  
Learning Mechanisms ◽  
Sequential Processing ◽  
Linguistic Patterns ◽  
Syntax Acquisition ◽  
Distinct Sequence ◽  
Hearing Children

The ability to acquire spoken language depends in part on a sensitivity to the sequential regularities contained within linguistic input. In this chapter, the authors propose that language learning operates via two distinct sequence-learning processes: probabilistic sequence learning, which supports the acquisition of syntax and other structured linguistic patterns, and repetition sequence learning, which supports word learning. First, the authors review work from their lab and others illustrating that performance on tasks that require participants to learn non-linguistic sequential patterns is empirically associated with different measures of language processing. Second, they present recent work from their lab specifically highlighting the role played by probabilistic sequence learning for acquiring syntax in a sample of deaf and hard-of-hearing children. Finally, the authors demonstrate that the learning of repeating sequences is related to vocabulary development in these children. These findings suggest that there may be at least two relatively distinct domain-general sequential processing skills, with each supporting a different aspect of language acquisition.

scholarly journals Two Distinct Sequence Learning Mechanisms for Syntax Acquisition and Word Learning

2014 ◽  
pp. 540-560
Author(s):  
Anne McClure Walk ◽  
Christopher M. Conway
Keyword(s):  
Language Learning ◽  
Language Processing ◽  
Word Learning ◽  
Sequence Learning ◽  
Learning Mechanisms ◽  
Sequential Processing ◽  
Linguistic Patterns ◽  
Syntax Acquisition ◽  
Distinct Sequence ◽  
Hearing Children

The ability to acquire spoken language depends in part on a sensitivity to the sequential regularities contained within linguistic input. In this chapter, the authors propose that language learning operates via two distinct sequence-learning processes: probabilistic sequence learning, which supports the acquisition of syntax and other structured linguistic patterns, and repetition sequence learning, which supports word learning. First, the authors review work from their lab and others illustrating that performance on tasks that require participants to learn non-linguistic sequential patterns is empirically associated with different measures of language processing. Second, they present recent work from their lab specifically highlighting the role played by probabilistic sequence learning for acquiring syntax in a sample of deaf and hard-of-hearing children. Finally, the authors demonstrate that the learning of repeating sequences is related to vocabulary development in these children. These findings suggest that there may be at least two relatively distinct domain-general sequential processing skills, with each supporting a different aspect of language acquisition.

Understanding Bilingual Word Learning: The Role of Phonotactic Probability and Phonological Neighborhood Density

2017 ◽  
Vol 60 (12) ◽  
pp. 3551-3560 ◽  
Author(s):  
Vishnu KK Nair ◽  
Britta Biedermann ◽  
Lyndsey Nickels
Keyword(s):  
Language Learning ◽  
Word Learning ◽  
Neighborhood Density ◽  
Learning Mechanisms ◽  
Phonotactic Probability ◽  
Learning Abilities ◽  
Phonological Neighborhood Density ◽  
Bilingual Speakers ◽  
Novel Word Learning ◽  
Phonological Neighborhood

Purpose Previous research has shown that the language-learning mechanism is affected by bilingualism resulting in a novel word learning advantage for bilingual speakers. However, less is known about the factors that might influence this advantage. This article reports an investigation of 2 factors: phonotactic probability and phonological neighborhood density. Method Acquisition of 15 novel words varying in phonotactic probability and phonological neighborhood density was examined in high-proficiency, early onset, Mandarin–English bilinguals and English monolinguals. Results Both bilinguals and monolinguals demonstrated a significant effect of phonotactic probability and phonological neighborhood density. Novel word learning improved when the phonological neighborhood density was higher; in contrast, higher phonotactic probability resulted in worse learning. Although the bilingual speakers showed significantly better novel word learning than monolingual speakers, this did not interact with phonotactic probability and phonological neighborhood density manipulations. Conclusion Both bilingual and monolingual word learning abilities are constrained by the same learning mechanisms. However, bilingual advantages may be underpinned by more effective allocation of cognitive resources due to their dual language experience.

Words Go Together Like ‘Bread and Butter’: The Rapid, Automatic Acquisition of Lexical Patterns

2020 ◽  
Author(s):  
Kathy Conklin ◽  
Gareth Carrol
Keyword(s):  
Short Stories ◽  
Language Learning ◽  
Language Processing ◽  
Processing System ◽  
Native English Speakers ◽  
English Speakers ◽  
Formulaic Language ◽  
Spoken Discourse ◽  
Linguistic Patterns ◽  
Reversed Order

Abstract While it is possible to express the same meaning in different ways (‘bread and butter’ versus ‘butter and bread’), we tend to say things in the same way. As much as half of spoken discourse is made up of formulaic language or linguistic patterns. Despite its prevalence, little is known about how the processing system treats novel patterns and how rapidly a sensitivity to them arises in natural contexts. To address this, we monitored native English speakers’ eye movements when reading short stories containing existing (conventional) patterns (‘time and money’), seen once, and novel patterns (‘wires and pipes’), seen one to five times. Subsequently, readers saw both existing and novel phrases in the reversed order (‘money and time’; ‘pipes and wires’). In four to five exposures, much like existing lexical patterns, novel ones demonstrate a processing advantage. Sensitivity to lexical patterns—including the co-occurrence of lexical items and the order in which they occur—arises rapidly and automatically during natural reading. This has implications for language learning and is in line with usage-based models of language processing.

Foundations of Language Development in Deaf and Hard-of-Hearing Infants

2020 ◽  
pp. 19-32
Author(s):  
Dani Levine ◽  
Daniela Avelar ◽  
Roberta Michnick Golinkoff ◽  
Kathy Hirsh-Pasek ◽  
Derek M. Houston
Keyword(s):  
Language Development ◽  
Language Learning ◽  
Cochlear Implants ◽  
Hearing Aids ◽  
Hard Of Hearing ◽  
Social Processes ◽  
Effective Interventions ◽  
Hearing Devices ◽  
Language Input ◽  
Hearing Children

Copious evidence indicates that, even in the first year of life, children’s language development is beginning and is impacted by a wide array of cognitive and social processes. The extent to which these processes are dependent on early language input is a critical concern for most deaf and hard-of-hearing (DHH) children, who, unlike hearing children, are usually not immersed in a language-rich environment until effective interventions, such as hearing aids or cochlear implants, are implemented. Importantly, some cognitive and social processes are not dependent on the early availability of language input and begin to develop before children are fitted for hearing aids or cochlear implants. Interventions involving parent training may be helpful for enhancing social underpinnings of language and for maximizing DHH children’s language learning once effective hearing devices are in place. Similarly, cognitive training for DHH children may also provide benefit to bolster language development.

scholarly journals Building natural language processing tools for Runyakitara

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Fridah Katushemererwe ◽  
Andrew Caines ◽  
Paula Buttery
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Learning ◽  
Language Processing ◽  
Primary Data ◽  
Computer Assisted ◽  
Endangered Languages ◽  
Test Case ◽  
Short Supply ◽  
Linguistic Resources

AbstractThis paper describes an endeavour to build natural language processing (NLP) tools for Runyakitara, a group of four closely related Bantu languages spoken in western Uganda. In contrast with major world languages such as English, for which corpora are comparatively abundant and NLP tools are well developed, computational linguistic resources for Runyakitara are in short supply. First therefore, we need to collect corpora for these languages, before we can proceed to the design of a spell-checker, grammar-checker and applications for computer-assisted language learning (CALL). We explain how we are collecting primary data for a new Runya Corpus of speech and writing, we outline the design of a morphological analyser, and discuss how we can use these new resources to build NLP tools. We are initially working with Runyankore–Rukiga, a closely-related pair of Runyakitara languages, and we frame our project in the context of NLP for low-resource languages, as well as CALL for the preservation of endangered languages. We put our project forward as a test case for the revitalization of endangered languages through education and technology.

scholarly journals 3D Avatar Approach for Continuous Sign Movement Using Speech/Text

2021 ◽  
Vol 11 (8) ◽  
pp. 3439
Author(s):  
Debashis Das Chakladar ◽  
Pradeep Kumar ◽  
Shubham Mandal ◽  
Partha Pratim Roy ◽  
Masakazu Iwamura ◽  
...  
Keyword(s):  
Language Learning ◽  
Sign Language ◽  
Language Processing ◽  
Hearing Impaired ◽  
Learning System ◽  
English Sentence ◽  
Impaired People ◽  
Indian Sign Language ◽  
Impaired Person ◽  
3D Avatar

Sign language is a visual language for communication used by hearing-impaired people with the help of hand and finger movements. Indian Sign Language (ISL) is a well-developed and standard way of communication for hearing-impaired people living in India. However, other people who use spoken language always face difficulty while communicating with a hearing-impaired person due to lack of sign language knowledge. In this study, we have developed a 3D avatar-based sign language learning system that converts the input speech/text into corresponding sign movements for ISL. The system consists of three modules. Initially, the input speech is converted into an English sentence. Then, that English sentence is converted into the corresponding ISL sentence using the Natural Language Processing (NLP) technique. Finally, the motion of the 3D avatar is defined based on the ISL sentence. The translation module achieves a 10.50 SER (Sign Error Rate) score.

Learning Through Processing: Toward an Integrated Approach to Early Word Learning

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Stephan C. Meylan ◽  
Elika Bergelson
Keyword(s):  
Word Learning ◽  
Empirical Work ◽  
Integrated Approach ◽  
Linguistic Knowledge ◽  
Annual Review ◽  
Publication Date ◽  
Learning Mechanisms ◽  
Lexical Knowledge ◽  
Novel Word Learning

Children's linguistic knowledge and the learning mechanisms by which they acquire it grow substantially in infancy and toddlerhood, yet theories of word learning largely fail to incorporate these shifts. Moreover, researchers’ often-siloed focus on either familiar word recognition or novel word learning limits the critical consideration of how these two relate. As a step toward a mechanistic theory of language acquisition, we present a framework of “learning through processing” and relate it to the prevailing methods used to assess children's early knowledge of words. Incorporating recent empirical work, we posit a specific, testable timeline of qualitative changes in the learning process in this interval. We conclude with several challenges and avenues for building a comprehensive theory of early word learning: better characterization of the input, reconciling results across approaches, and treating lexical knowledge in the nascent grammar with sufficient sophistication to ensure generalizability across languages and development. Expected final online publication date for the Annual Review of Linguistics, Volume 8 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Beyond robotic speech: mutual benefits to cognitive psychology and artificial intelligence from the study of multimodal communication

2020 ◽  
Author(s):  
Beata Grzyb ◽  
Gabriella Vigliocco
Keyword(s):  
Artificial Intelligence ◽  
Cognitive Psychology ◽  
Language Learning ◽  
Language Processing ◽  
Computational Models ◽  
Eye Gaze ◽  
Human Communication ◽  
Embodied Agents ◽  
Human Machine Interaction ◽  
Neural Underpinnings

Language has predominately been studied as a unimodal phenomenon - as speech or text without much consideration of its physical and social context – this is true both in cognitive psychology/psycholinguistics as well as in artificial intelligence. However, in everyday life, language is most often used in face-to-face communication and in addition to structured speech it comprises a dynamic system of multiplex components such as gestures, eye gaze, mouth movements and prosodic modulation. Recently, cognitive scientists have started to realise the potential importance of multimodality for the understanding of human communication and its neural underpinnings; while AI scientists have begun to address how to integrate multimodality in order to improve communication between human and artificial embodied agent. We review here the existing literature on multimodal language learning and processing in humans and the literature on perception of artificial agents, their comprehension and production of multimodal cues and we discuss their main limitations. We conclude by arguing that by joining forces AI scientists can improve the effectiveness of human-machine interaction and increase the human-likeness and acceptance of embodied agents in society. In turn, computational models that generate language in artificial embodied agents constitute a unique research tool to investigate the underlying mechanisms that govern language processing and learning in humans.

scholarly journals Circadian rhythms and second language performance

2017 ◽  
Vol 7 (1) ◽  
pp. 47-60
Author(s):  
Kees De Bot ◽  
Fang Fang
Keyword(s):  
Second Language ◽  
Language Learning ◽  
Lexical Access ◽  
Language Processing ◽  
Language Performance ◽  
Intraindividual Variation ◽  
The Hours ◽  
L1 And L2 ◽  
The Impact ◽  
Second Language Performance

Human behavior is not constant over the hours of the day, and there are considerable individual differences. Some people raise early and go to bed early and have their peek performance early in the day (“larks”) while others tend to go to bed late and get up late and have their best performance later in the day (“owls”). In this contribution we report on three projects on the role of chronotype (CT) in language processing and learning. The first study (de Bot, 2013) reports on the impact of CT on language learning aptitude and word learning. The second project was reported in Fang (2015) and looks at CT and executive functions, in particular inhibition as measured by variants of the Stroop test. The third project aimed at assessing lexical access in L1 and L2 at preferred and non-preferred times of the day. The data suggest that there are effects of CT on language learning and processing. There is a small effect of CT on language aptitude and a stronger effect of CT on lexical access in the first and second language. The lack of significance for other tasks is mainly caused by the large interindividual and intraindividual variation.

Adapting SVM for data sparseness and imbalance: a case study in information extraction

2009 ◽  
Vol 15 (2) ◽  
pp. 241-271 ◽  
Author(s):  
YAOYONG LI ◽  
KALINA BONTCHEVA ◽  
HAMISH CUNNINGHAM
Keyword(s):  
Active Learning ◽  
Language Learning ◽  
Information Extraction ◽  
Language Processing ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Support Vector ◽  
Passive Learning ◽  
Wide Range

AbstractSupport Vector Machines (SVM) have been used successfully in many Natural Language Processing (NLP) tasks. The novel contribution of this paper is in investigating two techniques for making SVM more suitable for language learning tasks. Firstly, we propose an SVM with uneven margins (SVMUM) model to deal with the problem of imbalanced training data. Secondly, SVM active learning is employed in order to alleviate the difficulty in obtaining labelled training data. The algorithms are presented and evaluated on several Information Extraction (IE) tasks, where they achieved better performance than the standard SVM and the SVM with passive learning, respectively. Moreover, by combining SVMUM with the active learning algorithm, we achieve the best reported results on the seminars and jobs corpora, which are benchmark data sets used for evaluation and comparison of machine learning algorithms for IE. In addition, we also evaluate the token based classification framework for IE with three different entity tagging schemes. In comparison to previous methods dealing with the same problems, our methods are both effective and efficient, which are valuable features for real-world applications. Due to the similarity in the formulation of the learning problem for IE and for other NLP tasks, the two techniques are likely to be beneficial in a wide range of applications1.

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