Recent advances in Machine Learning (ML), Artificial Intelligence (AI), Natural Language Processing (NLP), and web technologies provide tools that can automate and improve studies of L1 and L2 spoken language learning. We have been applying these new developments to improve the scope and power of the 14 databases in TalkBank, including databases such as CHILDES, AphasiaBank, DementiaBank, and SLABank. This presentation will explain the processes and results of six of these developments:
1. We have configured advances in automatic speech recognition (ASR) to speed and improve transcription of language samples. We use our Batchalign Python script to go quickly from raw audio to a first-draft CHAT transcript for inclusion in CHILDES, SLABank, or other TalkBank databases.
2. We have used Batchalign2 to apply Universal Dependency grammars to tag new and older transcripts in 28 languages for part-of-speech, morphological features, and grammatical dependency relations. These new resources open possibilities for deeper crosslinguistic study of language learning.
3. We have used a new Batchalign method for forced alignment of media to transcript to align hundreds of hours of transcripts in TalkBank to the related media on the utterances and word level for playback and analysis of fluency and interactional patterns.
4. The TalkBankDB system allows for systematic database queries across all materials in TalkBank
5. The Collaborative Commentary system allows for greater in-depth study and reliability checking for codes and comments on conversational patterns, error types, gestures, and learning patterns.
6. The new Phon4 program supports automatic detailed cross-tier analyses for lexical, morphological, syntactic, phonological, and fluency structures.
Next, we will consider the relation between learning methods for AI large language models (LLMs) such as ChatGPT and human language learning. Although LLMs and humans learn in markedly different ways, the resultant abilities are similar in some regards. We will consider ways in which modifications of LLMs could improve their use as models of human language learning.
A comprehensive theory of the human capacity for language must be flexible enough to capture the fact that human children can acquire any one of the world’s 7000+ diverse languages, and more often than not acquire more than one language at a time. However, what we know about how children acquire language is heavily skewed towards English and a handful of other (mostly related) languages (Kidd & Garcia, 2022), studied primarily in monolingual contexts. In this talk I discuss the current state of language coverage in the field of child language acquisition and juxtapose it against a parallel and concerning trend: the rapid rate at which languages are becoming endangered and lost. I then argue that concentrating on studying the acquisition of threatened languages can have the dual effect of widening the number of languages for which we have acquisition data, thus leading to more robust theoretical development, and can also help stem the flow of language loss by investing in the transmission of language to future generations.
Globally, around 1% of the population is autistic, with approximately 30% of autistic individuals being nonspeaking, despite various interventions1–4. Challenges with spoken language impact communication, social interaction, academic performance, and overall quality of life5–8. These difficulties often lead to greater reliance on health and other services9–12. Improving spoken language outcomes for autistic individuals can enhance their quality of life and reduce these global support costs13.
Research has shown that music-making and music training can improve auditory processing, sensorimotor integration, motor and imitative skills, cognitive function, social interaction, communication, and most notably, language processing14. In this talk, I will share findings from our proof-of-concept project, MAP (music-assisted programmes), funded by the European Research Council (ERC). This project explored how music can support language learning in autistic children.
We conducted a feasibility randomised controlled trial (RCT) using parent-mediated, music-based telehealth interventions to improve spoken language in autistic children aged 2-5 with minimal verbal skills15,16. Our results showed that children in the MAP group demonstrated increased social responsiveness and better parent-child interactions compared to those in the control group, who received a social communication intervention.
I will discuss the potential mechanisms behind MAP's effectiveness in enhancing language acquisition in autism. Additionally, I will outline our plans for a global project aimed at evaluating the efficacy of an AI-powered music app in supporting language learning in autistic children, focusing on both English and Mandarin speakers.
References
1 Hampton, L. H. et al. Journal of Intellectual Disability Research 60, 444–463 (2016)
2 Oono, I. P. et al. Cochrane Database Syst Rev CD009774 (2013)
3 Tager-Flusberg, H. et al. Autism Res 6, 468–478 (2013)
4 Zeidan, J. et al. Autism Research 15, 778–790 (2022)
5 Billstedt, E. et al. J Autism Dev Disord 35, 351–360 (2005)
6 Moss, P. et al. J Autism Dev Disord 47, 1830–1837 (2017)
7 Howlin, P. et al. J Child Psychol Psychiatry 45, 212–229 (2004)
8 Howlin, P. et al. J Child Psychol Psychiatry 55, 49–58 (2014)
9 Buescher, A. V. S. et al. JAMA Pediatr 168, 721–728 (2014)
10 Taneja, A. et al. Value in Health 20, A712 (2017)
11 Tinelli, M. et al. European Psychiatry 66, e76 (2023)
12 Bieleninik, Ł. et al. Brain Sciences 11, 340 (2021)
13 Gotham, K. et al. Autism 19, 794–804 (2015)
14 Kraus, N. et al. Neuroscientist (2016)
15 Williams, T. I. et al. Pilot and Feasibility Studies 7, 182 (2021)
16 Williams, T. I. et al. Autism 13623613241233804 (2024)
The talk explores anaphora resolution in the grammars of Mandarin monolingual adults, monolingual children and L2 speakers through two independent picture judgment tasks. Anaphora resolution has aroused much interest in L1 and L2 acquisition due to its complexity. Although there are many studies on anaphora resolution in Romance pro-drop languages, studies on Mandarin Chinese are scarce (e.g., Zhao, 2014; Su, 2020). Mandarin Chinese is of particular interest to the understanding of anaphora resolution as it differs from Romance pro-drop languages in terms of the interplay of syntax, semantics and discourse. Both the overt pronoun and the null element analysed as a syntax-discourse interface category topic has a subject preference in forward anaphora due to the discourse-prominent nature of Mandarin, while the overt pronoun is constrained by a language-specific syntactic constraint Principle C’, resulting in the obligatory disjoint interpretation in backward anaphora (Huang and Lin, 2021; Zhao 2014). Most studies on L1 and L2 acquisition of anaphora resolution in Romance pro-drop languages have found that L1 children and L2 adult learners have more difficulty in interpreting overt pronouns. which has been attributed to processing difficulty with reference to the Position of the Antecedent Strategy (Carminati, 2002). Our studies seem to show that Mandarin anaphora resolution poses different challenges to L1 and L2 learners. Highly proficient English-speaking L2 learners have acquired the interpretation of the null element but showed divergent resolution patterns for the overt pronoun.
This indicates that L2 learners can acquire syntax-discourse interface categories but may have problem with language-specific syntactic constraints. By contrast, L1 children acquired the interpretation of overt pronouns at 8-10 despite the lack of convergence at 4-6. Their interpretation of the null element was still developing at 8-10. We argue that syntax-discourse interface categories may be subject to even further delays than language-specific syntactic constraints in L1 acquisition, which are acquired later than language universal constraints (Su 2020).
References:
Carminati, M.N. (2002). The processing of Italian subject pronouns. PhD Thesis, University of Massachusetts Amherst.
Huang, C.-T. & Jo-Wang, L. (2021). Quantificational binding without surface c-command in Mandarin Chinese. In Si, F. .& L. Rizzi (eds) Current Issues in Syntactic Cartography: A crosslinguistic perspective (pp.183-216), John Benjamins.
Su, Y.-C. (2020). Backward/forward anaphora in child and adult Mandarin Chinese, Language Acquisition 27, 187-216.
Zhao, L. X. (2014). Ultimate attainment in anaphora resolution, Second Language Research 30, 381–407.
Speech Therapy Unit; Research Centre for Language, Cognition, and Neuroscience, Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University; The Hong Kong Polytechnic University – Peking University Research Centre in Chinese Linguistics
Developmental Language Disorder (DLD) is a specific language learning disability that affects 2 children in every class of 30, making it more prevalent than Autism. It negatively impacts an individual's academic outcomes, mental health, and career development. However, when identified and provided with appropriate language intervention, DLD has been shown to respond well to treatment.
Identifying DLD in bilingual children has been a major challenge in speech pathology and clinical linguistics. One needs to distinguish between neurotypically developing (NTD) bilingual children who are not as proficient in the target language due to reduced exposure despite full language potential, and children with genuine DLD who are not fully proficient in any language because of processing and learning limitations. Furthermore, since bilingual children with DLD have language difficulties in both languages, and children often need to learn two or even three languages when they enter school, dual language intervention supporting the development of their home language and school language would be beneficial. However, speech pathologists and educators often find it challenging to effectively support children in their development of these two languages.
In this talk, I will present my recent research and plans that emphasize the importance of setting a reference standard in identifying DLD in bilingual Chinese-speaking children, as well as the development of progress monitoring tools and intervention materials to support dual language intervention for bilingual Cantonese-English speaking children with speech, language, and communication needs. Importantly, although the studies will be presented in a Chinese and bilingual context, these instruments (assessing narrative comprehension and production, lexical comprehension and production, nonword repetition, and morphosyntactic abilities) and the dual language intervention program could be further adapted to other languages and cultures, to support the identification of and dual language intervention for bilingual children with DLD in other target languages.
This study examines the emergence and frequency of English existential quantifiers a(n), some, also, and again, and universal quantifiers all, every, only, and always in child language, based on longitudinal data from 10 English-speaking children aged between 1;6 and 5;2 and their caregivers. We found that input frequency plays a significant role in the acquisition of these quantifiers. The children resemble their caregivers in using the quantifiers in terms of frequency not only at the concrete lexical level but also at the abstract level (i.e. syntactic distribution). Input frequency parallels with the earlier emergence of existential quantification than universal quantification (as instantiated by a slightly earlier than all in most of the children), the earlier emergence of existential D(eterminer)-quantification than A(dverbial)-quantification (a slightly earlier than again in most of the children), and may explain the emergence and frequency of most quantifiers in child language (except for the underuse of a and only, and the early emergence of again). However, there are quantitative and qualitative differences between the children and adults in using quantifiers. For instance, the children show a stronger tendency to put A-quantifiers again, also and always at the periphery of a clause than adults. The most striking difference is children's non-target production, such as overpluralization (*Put some mores in) and agreement errors (*A strawberrys). Children’s non-adult-like patterns may be attributed to their developing grammatical system and cognitive factors.
Keywords: existential quantifier, universal quantifier, D(eterminer)-quantification, A(dverbial)-quantification, input frequency, overpluralization
Wenchun Yang1, Angel Chan2, Natalia Gagarina3
1School of Foreign Studies, Xi'an Jiaotong University, Xi'an
2Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, HK
3Leibniz-Centre General Linguistics (ZAS), Berlin
This talk features children raised in a distinctly unique linguistic and sociocultural environment. They typically live in remote rural areas and are 'left behind' as their parent(s) go to cities for employment (SCPRC 2016). Narrative production data from Yi (a Tibeto-Burman language) and Kam (a Kam-Tai language) children were collected using LITMUS MAIN (Gagarina et al., 2019). Thirty-nine (Mage=6;4, 19 left behind vs 20 non-left behind) Yi children from Yunnan and 55 Kam (Mage=7;0, 23 two parents-left vs 32 one parent-left) children from Guangxi were recruited. Each child (re)told four picture-based stories. Narrative competence was measured by two macrostructure measures (story structure (SS), story complexity (SC)), and the use of internal state terms (IS). The first round of analysis focused on Yi children’s L2 Mandarin and Kam children’s L1 Kam. Preliminary results showed that left-behind experience as a whole negatively predicted expressive narrative competence. Left behind children scored lower in all three measures, particularly IS, than non-left behind children, and two-parents left children scored lowest. These findings offer some important implications for policies and practices pertinent to this group of underprivileged population.
The Chinese University of Hong Kong
szewingli@cuhk.edu.hk
(Ms. LI Sze Wing)
(852) 3943-5174
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