Hasil untuk "English language"

M. Aceto, D. Crystal

David Crystal, a professor of Linguistics and an authority on English Language, has written a major work on the status of English as a global language. This book is an informed estimate by one of the distinguished scholars of language about the future of English…

Viet Dac Lai, Nghia Trung Ngo, Amir Pouran Ben Veyseh et al.

Over the last few years, large language models (LLMs) have emerged as the most important breakthroughs in natural language processing (NLP) that fundamentally transform research and developments in the field. ChatGPT represents one of the most exciting LLM systems developed recently to showcase impressive skills for language generation and highly attract public attention. Among various exciting applications discovered for ChatGPT in English, the model can process and generate texts for multiple languages due to its multilingual training data. Given the broad adoption of ChatGPT for English in different problems and areas, a natural question is whether ChatGPT can also be applied effectively for other languages or it is necessary to develop more language-specific technologies. The answer to this question requires a thorough evaluation of ChatGPT over multiple tasks with diverse languages and large datasets (i.e., beyond reported anecdotes), which is still missing or limited in current research. Our work aims to fill this gap for the evaluation of ChatGPT and similar LLMs to provide more comprehensive information for multilingual NLP applications. While this work will be an ongoing effort to include additional experiments in the future, our current paper evaluates ChatGPT on 7 different tasks, covering 37 diverse languages with high, medium, low, and extremely low resources. We also focus on the zero-shot learning setting for ChatGPT to improve reproducibility and better simulate the interactions of general users. Compared to the performance of previous models, our extensive experimental results demonstrate a worse performance of ChatGPT for different NLP tasks and languages, calling for further research to develop better models and understanding for multilingual learning.

M. Ahmadi

The use of technology has become an important part of the learning process in and out of the class. Every language class usually uses some form of technology. Technology has been used to both help and improve language learning. Technology enables teachers to adapt classroom activities, thus enhancing the language learning process. Technology continues to grow in importance as a tool to help teachers facilitate language learning for their learners. This study focuses on the role of using new technologies in learning English as a second/foreign language. It discussed different attitudes which support English language learners to increase their learning skills through using technologies. In this paper, the researcher defined the term technology and technology integration, explained the use of technology in language classroom, reviewed previous studies on using technologies in improving language learning skills, and stated certain recommendations for the better use of these technologies, which assist learners in improving their learning skills. The literature review indicated that the effective use of new technologies improves learners’ language learning skills.

Ling Wei

Introduction This mixed methods study examines the effects of AI-mediated language instruction on English learning achievement, L2 motivation, and self-regulated learning among English as a Foreign Language (EFL) learners. It addresses the increasing interest in AI-driven educational technologies and their potential to revolutionize language instruction. Methods Two intact classes, consisting of a total of 60 university students, participated in this study. The experimental group received AI-mediated instruction, while the control group received traditional language instruction. Pre-tests and post-tests were administered to evaluate English learning achievement across various domains, including grammar, vocabulary, reading comprehension, and writing skills. Additionally, self-report questionnaires were employed to assess L2 motivation and self-regulated learning. Results Quantitative analysis revealed that the experimental group achieved significantly higher English learning outcomes in all assessed areas compared to the control group. Furthermore, they exhibited greater L2 motivation and more extensive utilization of self-regulated learning strategies. These results suggest that AI-mediated instruction positively impacts English learning achievement, L2 motivation, and self-regulated learning. Discussion Qualitative analysis of semi-structured interviews with 14 students from the experimental group shed light on the transformative effects of the AI platform. It was found to enhance engagement and offer personalized learning experiences, ultimately boosting motivation and fostering self-regulated learning. These findings emphasize the potential of AI-mediated language instruction to improve language learning outcomes, motivate learners, and promote autonomy. Conclusion This study contributes to evidence-based language pedagogy, offering valuable insights to educators and researchers interested in incorporating AI-powered platforms into language classrooms. The results support the notion that AI-mediated language instruction holds promise in revolutionizing language learning, and it highlights the positive impact of AI-driven educational technologies in the realm of language education.

Amr M. Mohamed

Domen Vreš, Tjaša Arčon, Timotej Petrič et al.

Large language models (LLMs) have become an essential tool for natural language processing and artificial intelligence in general. Current open-source models are primarily trained on English texts, resulting in poorer performance on less-resourced languages and cultures. We present a set of methodological approaches necessary for the successful adaptation of an LLM to a less-resourced language, and demonstrate them using the Slovene language. We present GaMS3-12B, a generative model for Slovene with 12 billion parameters, and demonstrate that it is the best-performing open-source model for Slovene within its parameter range. We adapted the model to the Slovene language using three-stage continual pre-training of the Gemma 3 model, followed by two-stage supervised fine-tuning (SFT). We trained the model on a combination of 140B Slovene, English, Bosnian, Serbian, and Croatian pretraining tokens, and over 200 thousand English and Slovene SFT examples. We evaluate GaMS3-12B on the Slovenian-LLM-Eval datasets, English-to-Slovene translation, and the Slovene LLM arena. We show that the described model outperforms 12B Gemma 3 across all three scenarios and performs comparably to much larger commercial GPT-4o in the Slovene LLM arena, achieving a win rate of over 60 %.

Mohammad Hossein Arefian

Language teacher education programs can become more reflective, inclusive, collaborative, situated, and inquiry-based. One such professional approach to incorporate these characters can be through personalized language teacher education (PLTE). Due to the importance of using AI and professional learning communities (PLCs) for developing a personalized teacher education, this study explored how AI-enhanced PLCs could be leveraged to create a more responsive, inclusive, and personalized teacher education. Still, a significant gap exists in understanding how AI can be specially integrated into PLCs to create personalized pathways for ELT preservice teachers, mainly in under-resourced contexts. To conduct this exploratory case study, 8 Iranian English language teaching (ELT) pre-service teachers were purposively selected from a teacher education university. Data was collected from group discussion, artifacts, and interviews, and the result of the thematic analysis revealed that AI-enhanced PLCs fostered personalized, reflective, and collaborative development by addressing individual teaching needs and providing innovative strategies. By addressing individual teaching needs and providing innovative instructional strategies, AI facilitated a dynamic learning environment. However, effective integration required overcoming challenges like limited AI literacy and contextual mismatches, highlighting the potential for tailored, impactful education. This study can inform teacher educators, policymakers, administrators, and teachers to integrate AI into their PLCs to develop a PLTE.

Ikuya Aizawa, H. Rose, Gene Thompson et al.

This article examines the relationship between Japanese undergraduate students’ English language proficiency and English language-related challenges faced when studying an international business course through English. It also examines English language proficiency thresholds students need to reach in each academic skill (i.e. reading, listening, speaking and writing) to experience a lower level of linguistic challenges. A total of 264 students were surveyed in Tokyo, Japan, and 13 follow-up interviews were conducted. Exploratory and confirmatory factor analyses confirmed the underlying factors in the EMI (English medium of instruction) Challenges Scale loaded onto a priori assumptions of dimensions falling along skill-based constructs. Analysis of questionnaire data revealed that English language proficiency (i.e. TOEIC score) was a statistically significant predictor of challenges in the EMI programme. While no clear discernible threshold was observed, the differences in perceived ease of study at different levels of English proficiency influenced the challenges students reported for each academic skill. Interview data uncovered the multi-faceted nature of how the thresholds are determined not only by language proficiency but also by other factors, such as prior content knowledge, motivation, and the classroom learning environment. Practical implications for pedagogy are also discussed.

Ko Konno, M. Akasaka, Chieko Koshida et al.

Abstract Meta‐analysis plays a crucial role in syntheses of quantitative evidence in ecology and biodiversity conservation. The reliability of estimates in meta‐analyses strongly depends on unbiased sampling of primary studies. Although earlier studies have explored potential biases in ecological meta‐analyses, biases in reported statistical results and associated study characteristics published in different languages have never been tested in environmental sciences. We address this knowledge gap by systematically searching published meta‐analyses and comparing effect‐size estimates between English‐ and Japanese‐language studies included in existing meta‐analyses. Of the 40 published ecological meta‐analysis articles authored by those affiliated to Japanese institutions, we find that three meta‐analysis articles searched for studies in the two languages and involved sufficient numbers of English‐ and Japanese‐language studies, resulting in four eligible meta‐analyses (i.e., four meta‐analyses conducted in the three meta‐analysis articles). In two of the four, effect sizes differ significantly between the English‐ and Japanese‐language studies included in the meta‐analyses, causing considerable changes in overall mean effect sizes and even their direction when Japanese‐language studies are excluded. The observed differences in effect sizes are likely attributable to systematic differences in reported statistical results and associated study characteristics, particularly taxa and ecosystems, between English‐ and Japanese‐language studies. Despite being based on a small sample size, our findings suggest that ignoring non‐English‐language studies may bias outcomes of ecological meta‐analyses, due to systematic differences in study characteristics and effect‐size estimates between English‐ and non‐English languages. We provide a list of actions that meta‐analysts could take in the future to reduce the risk of language bias.

April Baker-Bell

ABSTRACT In this article, the author historicizes the argument about Black Language in the classroom to contextualize the contemporary linguistic inequities that Black students experience in English Language Arts (ELA) classroom. Next, the author describes anti-black linguistic racism and interrogates the notion of academic language. Following this, the author provides an ethnographic snapshot that shows how Black students in a ninth grade English Language Arts (ELA) class perceptions of Black Language reflected internalized anti-black linguistic racism. The author offers Anti-Racist Black Language Pedagogy as an approach that English Language Arts teachers can implement in an effort to dismantle anti-black linguistic racism and white cultural and linguistic hegemony in their classrooms using Angie Thomas’ (2017) novel The Hate U Give. The author concludes with thoughts about how an Anti-Racist Black Language pedagogy can help ELA students develop useful critical capacities.

Richard Kimera, Dongnyeong Heo, Daniela N. Rim et al.

In this paper,we explore the application of Back translation (BT) as a semi-supervised technique to enhance Neural Machine Translation(NMT) models for the English-Luganda language pair, specifically addressing the challenges faced by low-resource languages. The purpose of our study is to demonstrate how BT can mitigate the scarcity of bilingual data by generating synthetic data from monolingual corpora. Our methodology involves developing custom NMT models using both publicly available and web-crawled data, and applying Iterative and Incremental Back translation techniques. We strategically select datasets for incremental back translation across multiple small datasets, which is a novel element of our approach. The results of our study show significant improvements, with translation performance for the English-Luganda pair exceeding previous benchmarks by more than 10 BLEU score units across all translation directions. Additionally, our evaluation incorporates comprehensive assessment metrics such as SacreBLEU, ChrF2, and TER, providing a nuanced understanding of translation quality. The conclusion drawn from our research confirms the efficacy of BT when strategically curated datasets are utilized, establishing new performance benchmarks and demonstrating the potential of BT in enhancing NMT models for low-resource languages.

Ali Mohammad Mossadeghrad, Shervin Mossavarali, Seyed Hamid Hosseini Neishabouri et al.

Objective: Employees’ management is the process of planning, organizing, directing, and controlling human resources within an organization, which is of great importance and it is necessary to pay attention to its various dimensions, including the provision, distribution, and retention of employees. In the present study, the challenges and problems in the field of personnel are examined and then solutions to reduce these challenges are discussed. Information sources and selected methods for study:  A scoping review was conducted in August 2023 to identify the challenges faced by healthcare workers and suggest appropriate solutions. The search was performed across English-language databases, including PubMed, Scopus, Web of Science, as well as Persian-language databases SID and Magiran, and search engines Google Scholar and Google. Title and abstract screening were independently carried out by three authors. After the screening and full-text review, data extraction was performed on 104 relevant studies. Data analysis was conducted using the Ritchie and Spencer framework analysis method. Results: A total of 27 challenges related to the healthcare workforce were identified and categorized into three domains: recruitment, distribution, and retention. The most critical challenges included unequal distribution of physicians, workforce shortages, job burnout, and migration of healthcare workers, all of which significantly impact the quality of healthcare services. Regarding workforce recruitment, key strategies included utilizing trainees, training non-specialist staff, and expanding educational capacities. To address distribution disparities, policies such as strengthening family physician programs, telemedicine, and offering financial incentives were proposed. In terms of workforce retention, effective solutions included welfare support, psychological counseling, stress management programs, and work-life balance initiatives. Conclusion: To improve the condition of health workers, policymakers must adopt an integrated, evidence-based approach addressing the three areas of recruitment, distribution, and retention. Moreover, proposed strategies should be contextualized based on each country's economic, social, and cultural conditions and implemented through intersectoral collaboration and sufficient resource allocation to ensure long-term effectiveness.

Ruby Bisht, Amit Kumar Uniyal, Amar Johri et al.

Abstract The research on work-life balance of teachers highlights the significant challenges faced by educators in balancing their professional and personal lives. The study emphasizes the evolving role of teachers, the impact of technology, and the increasing demands placed on them. It also underscores the importance of achieving a healthy work-life balance to reduce stress and improve job satisfaction. This study critically examines research papers and articles related to work-life balance of teachers published between 2014–2024 in the Scopus database. Bibliometric analysis was done with the help of a biblioshiny package of Rstusio and VoS viewer. Out of 554 articles, 112 were located after restricting the search to the English language and publications related to social science, psychology, business, management, and economics for the selected time (2014–2024). The research on the work-life balance of teachers has seen significant growth, especially during the COVID-19 pandemic. The findings highlight that the USA in countries, Fokkens-Bruinsma, M. in authors and Frontiers in Psychology in sources have the highest contribution of articles in related field. Key topics include “human,” “teaching,” “work-life balance,” “questionnaire,” and “education. This study provides a comprehensive overview of the research trends, key contributors, and important topics in the field of work-life balance of teachers. Moreover, it also highlights the growing interest in this area and the need for further research to address the challenges faced by educators. The findings suggest that implementing work-life policies and initiatives tailored to the teaching profession can help address these challenges and promote better well-being among teachers. Overall, the study calls for a greater focus on supporting teachers to ensure they can effectively manage their work and personal responsibilities.

Dino Dumančić

The study employed a mixed-methods approach to investigate the relationship between English language teachers’ teaching efficacy, emotional experiences, and situation and task-related procrastination. It aimed to explore both self-reported teaching self-efficacy beliefs and the factors influencing language teachers’ procrastination behaviors and emotions during task delay. A total of 305 Croatian EFL teachers participated in this study. Descriptive, correlation, and directed content analyses were carried out. According to the findings, the Croatian language teachers viewed themselves as highly effective in the classroom and they also reported engaging in procrastination infrequently. When inquired about language proficiency-related anxiety, they admitted having experienced it sporadically. Those confident in utilizing instructional strategies and implementing classroom management strategies procrastinated less and reported lower anxiety levels. Qualitative analysis revealed that demotivating or fatiguing tasks, especially administrative and testing-related ones, instigated procrastination, among others. When procrastinating, the teachers reported primarily unpleasant emotions, such as anxiety, nervousness, frustration, and guilt.

Nicos Sifakis

Leonidas Gee, Andrea Zugarini, Novi Quadrianto

To reduce the inference cost of large language models, model compression is increasingly used to create smaller scalable models. However, little is known about their robustness to minority subgroups defined by the labels and attributes of a dataset. In this paper, we investigate the effects of 18 different compression methods and settings on the subgroup robustness of BERT language models. We show that worst-group performance does not depend on model size alone, but also on the compression method used. Additionally, we find that model compression does not always worsen the performance on minority subgroups. Altogether, our analysis serves to further research into the subgroup robustness of model compression.

Yanis Labrak, Adrien Bazoge, Beatrice Daille et al.

Subword tokenization has become the prevailing standard in the field of natural language processing (NLP) over recent years, primarily due to the widespread utilization of pre-trained language models. This shift began with Byte-Pair Encoding (BPE) and was later followed by the adoption of SentencePiece and WordPiece. While subword tokenization consistently outperforms character and word-level tokenization, the precise factors contributing to its success remain unclear. Key aspects such as the optimal segmentation granularity for diverse tasks and languages, the influence of data sources on tokenizers, and the role of morphological information in Indo-European languages remain insufficiently explored. This is particularly pertinent for biomedical terminology, characterized by specific rules governing morpheme combinations. Despite the agglutinative nature of biomedical terminology, existing language models do not explicitly incorporate this knowledge, leading to inconsistent tokenization strategies for common terms. In this paper, we seek to delve into the complexities of subword tokenization in French biomedical domain across a variety of NLP tasks and pinpoint areas where further enhancements can be made. We analyze classical tokenization algorithms, including BPE and SentencePiece, and introduce an original tokenization strategy that integrates morpheme-enriched word segmentation into existing tokenization methods.

Phillip Richter-Pechanski, Philipp Wiesenbach, Dominic M. Schwab et al.

Automatic extraction of medical information from clinical documents poses several challenges: high costs of required clinical expertise, limited interpretability of model predictions, restricted computational resources and privacy regulations. Recent advances in domain-adaptation and prompting methods showed promising results with minimal training data using lightweight masked language models, which are suited for well-established interpretability methods. We are first to present a systematic evaluation of these methods in a low-resource setting, by performing multi-class section classification on German doctor's letters. We conduct extensive class-wise evaluations supported by Shapley values, to validate the quality of our small training data set and to ensure the interpretability of model predictions. We demonstrate that a lightweight, domain-adapted pretrained model, prompted with just 20 shots, outperforms a traditional classification model by 30.5% accuracy. Our results serve as a process-oriented guideline for clinical information extraction projects working with low-resource.

Kai-Wei Chang, Haibin Wu, Yu-Kai Wang et al.

Prompting has become a practical method for utilizing pre-trained language models (LMs). This approach offers several advantages. It allows an LM to adapt to new tasks with minimal training and parameter updates, thus achieving efficiency in both storage and computation. Additionally, prompting modifies only the LM's inputs and harnesses the generative capabilities of language models to address various downstream tasks in a unified manner. This significantly reduces the need for human labor in designing task-specific models. These advantages become even more evident as the number of tasks served by the LM scales up. Motivated by the strengths of prompting, we are the first to explore the potential of prompting speech LMs in the domain of speech processing. Recently, there has been a growing interest in converting speech into discrete units for language modeling. Our pioneer research demonstrates that these quantized speech units are highly versatile within our unified prompting framework. Not only can they serve as class labels, but they also contain rich phonetic information that can be re-synthesized back into speech signals for speech generation tasks. Specifically, we reformulate speech processing tasks into speech-to-unit generation tasks. As a result, we can seamlessly integrate tasks such as speech classification, sequence generation, and speech generation within a single, unified prompting framework. The experiment results show that the prompting method can achieve competitive performance compared to the strong fine-tuning method based on self-supervised learning models with a similar number of trainable parameters. The prompting method also shows promising results in the few-shot setting. Moreover, with the advanced speech LMs coming into the stage, the proposed prompting framework attains great potential.

Yurii Paniv, Dmytro Chaplynskyi, Nikita Trynus et al.

To build large language models for Ukrainian we need to expand our corpora with large amounts of new algorithmic tasks expressed in natural language. Examples of task performance expressed in English are abundant, so with a high-quality translation system our community will be enabled to curate datasets faster. To aid this goal, we introduce a recipe to build a translation system using supervised finetuning of a large pretrained language model with a noisy parallel dataset of 3M pairs of Ukrainian and English sentences followed by a second phase of training using 17K examples selected by k-fold perplexity filtering on another dataset of higher quality. Our decoder-only model named Dragoman beats performance of previous state of the art encoder-decoder models on the FLORES devtest set.