Language Technology Research Articles

Searching for A Better Educational System: Language Planning in Competency-Based School Curriculum

Purpose: The new European school curriculum focuses on innovation, competency-based learning and the integration of technology as ways to increase the quality of education and better prepare students for the challenges of the 21st century. This study investigates the effects of implementation of this curriculum, highlighting its benefits and potential challenges, especially in relation to the standard language. Theoretical Framework: The new European pre-university curriculum is explored in interdisciplinary approach, integrating the latest theories in teaching methodology with language planning and technology. Synthesizing the existing literature serves as a prerequisite for the analysis performed on the empirical research. Methodology: The impact of the implementation of the competence-based curriculum in Albanian school education are analyzed theoretically and empirically, emphasizing the advantages and shortcomings compared to the previous curriculum. Findings: The increase in the quality of school education is evident with the implementation of the new European curriculum, which prioritizes student-centered learning by incorporating technology into different learning scenarios. As shown by international evaluations such as Pisa, slow steps have been made in the national curriculum, which calls for further reforms by the educational authorities. Research Practical and Social Implication: The new national curriculum improves student outcomes and aligns with wider educational goals within the EU, influencing national identity and global social cohesion, which are important research areas. Originality/Value: This study values the competency-based curriculum as a necessity for preparing European students for a globally competitive world, pointing out some areas for improvements.

A Review of the Effects of Integrated Language, Science and Technology Interventions in Elementary Education on Student Achievement

This systematic literature review presents a review of the effects of integrated language arts, science and technology (ILS&T) instruction, with an inquiry- or design-based pedagogy, in elementary schools on student achievement. To this end, an overview of the characteristics of the 19 included studies and their interventions is first presented. Second, the effects of interventions in relation to the study characteristics and outcome variables were examined, by comparing the mean effect sizes. The findings demonstrate positive effects of ILS&T instruction for all reported student learning outcome variables. Third, the relation between characteristics of the intervention and effect sizes was analysed. Interventions with higher levels of integration, a short duration, and teacher professional development activities produce higher effect sizes. These findings are relevant for the design of ILS&T interventions. The analysis was challenged by a lack of detailed information in study and intervention descriptions, which prompts a call for scholars to provide more comprehensive information in their intervention studies.

Advancements and challenges in AI-driven language technologies: From natural language processing to language acquisition

This paper explores the evolution and impact of artificial intelligence (AI) in the realm of language technologies. We trace the historical development of language models in AI, starting from the rule-based systems of the 1960s to the sophisticated neural networks of today. The current state-of-the-art technologies, particularly transformer-based models like OpenAI's GPT series, are examined for their capabilities and limitations. We delve into the role of AI in language acquisition and learning, highlighting AI-driven language teaching tools such as Duolingo and Babbel, and discuss their effectiveness and challenges. Furthermore, the paper explores the significant contributions of AI in second language acquisition research, including the development of predictive models and sophisticated learner profiles. Ethical considerations and challenges, such as data privacy and potential biases, are also addressed. We discuss advancements in natural language processing (NLP) applications like text and sentiment analysis, speech recognition and generation, and machine translation, along with their cross-linguistic challenges. The conclusion envisions future directions for AI in language technologies, emphasizing the need for multimodal inputs, efficiency, and enhanced interpretability.

Establishing interoperability between EMF and MSDKVS: an M3-level-bridge to transform metamodels and models

AbstractMany powerful metamodeling platforms enabling model-driven software engineering (MDSE) exist, each with its strengths, weaknesses, functionalities, programming language(s), and developer community. Platform interoperability would enable users to exploit their mutual benefits. Such interoperability would allow the transformation of metamodels and models created in one platform into equivalent metamodels and models in other platforms. Language engineers could then freely choose the metamodeling platform without risking a lock-in effect. Two well-documented and publicly available metamodeling platforms are the eclipse modeling framework (EMF) and the modeling SDK for visual studio (MSDKVS). In this paper, we propose an M3-level-bridge (M3B) that establishes interoperability between EMF and MSDKVS on the abstract syntax level and on the graphical concrete syntax level. To establish such interoperability we (i) compare the two platforms, (ii) present a conceptual mapping between them, and (iii) implement a bidirectional transformation bridge including both the metamodel and model layer. We evaluate our approach by transforming a collection of publicly available metamodels and automatically generated or manually created models thereof. The transformation outcomes are then used to quantitatively and qualitatively evaluate the transformation’s validity, executability, and expressiveness.

BOOK REVIEW: TECHNOLOGY IN SECOND LANGUAGE WRITING: ADVANCES IN COMPOSING, TRANSLATION, WRITING PEDAGOGY AND DATA-DRIVEN LEARNING

This book provides a comprehensive exploration of the integration of technology in second language (L2) writing instruction. It examines the evolution of technology’s role in L2 learning, particularly in writing, where individual practice is crucial. The book consists of 11 chapters encompassing research and development reports on the application of technology, especially machine translation (MT), in the L2 writing learning process. While 90% of the content focuses on research results, a conceptual review in one chapter highlights the limitations of MT in English as a foreign language (EFL) writing. The book caters to L2 teachers and language learning researchers, offering valuable insights into using MT and wiki writing as effective learning instruments. Additionally, it presents novel strategies, such as using Wikipedia to hone critical thinking skills, and introduces handwriting automation for L2 writing assessment. Despite its merits, the book’s structure, content balance, and limited readership accessibility are areas requiring improvement. Nonetheless, its research findings and practical implications make it a valuable resource for L2 educators seeking to harness technology in writing instruction.

Exploring Cognitive Teaching Approaches for Inclusive Translation Online Classes: A Case Study During the COVID-19 Pandemic

This study explored teachers’ lived experiences of teaching translation inclusively online for the first time during the lockdown of the COVID-19 pandemic. The participants teach English and translation in the English Department, College of Languages and Translation, at Imam Mohammed Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia. Data were collected using a 5-point Likert questionnaire and semi-structured interviews. The study delved into the teaching approaches that teachers used to achieve course objectives, and the technology tools they embedded into the Learning Management System (LMS). The results indicated (73%) positive experiences, with (86.8%) finding the LMS effective for delivering instructional plans and (78.5%) reporting an increase in awareness of continuous technology education. The emergent themes of cognitive approaches included project-based instruction (89%), collaborative techniques (71%), conference-based strategies (64%), and constructivist approaches (50%). The results also showed improvements in students’ translation skills (80.7%), linguistic abilities (83.2%), social skills (78.5%), communicative skills (65.1%), and psychometric abilities (78.3%), indicating the positive effects of technology on cognitive domains. The findings illustrated that adopting effective instructional methods and integrating operative technological tools can significantly contribute to delivering enriched online content. The study recommends integrating Cultural Translation and Interpretation Studies (CTIS) and Cognitive Translation Studies (CTS) into professional development programs to enhance innovative teaching approaches for online settings. Consequently, the study contributes to language and translation education and technology by presenting an inclusive model that emphasizes the importance of incorporating translation theories, teaching methodologies, and appropriate technologies for effective online teaching.

Unearthing the latent assumptions inscribed into language tools: the cross-cultural benefits of applying a reflexive lens in co-design

ABSTRACT Language technology tools provide a promising way to teach, share, retain, and curate under-resourced language learning materials in community. The inclusion of language teachers working with communities increases the potential for designed tools to be adopted by those groups. However, there is little research concerning the adaptation of tools designed with one community to other languages. To identify the implications for such scalability, we ran workshops with the ‘Record and Write’ tool, developed as a versatile format for collection, curation, and use of under-resourced language learning materials in community. The process enabled teachers of languages with varying availability of teaching materials to reflect on some of the embedded complexities of adapting the tool to their context. This paper critically reflects on the design process of the tool and design lessons learned relating to language governance, the reflection of culture in database tools, conversational learning support, and differentiated needs for grammatical accuracy and annotation. Methodologically, the paper proposes a reflexive lens on co-design in cross-cultural contexts, identifying some of the latent assumptions embedded in technologies that emerge when tools are transposed to different language and learning contexts.

Research on Language Teaching Models under the Background of New Generation Information Technology

With the rapid development of the new generation of information technology, education has also ushered in the opportunity for change. In school language teaching, the application of new-generation information technology can provide students with more flexible and diverse learning methods, and promote the improvement of students' innovation and practical ability. At the same time, school language teaching, as an important part of basic education, also needs to constantly explore new teaching methods and technologies to further realize scientization in order to improve teaching effectiveness and quality. In this paper, we will discuss the research on the application of new generation information technology in school language teaching from the needs of school language teaching, respectively, from the learning content, teaching methods, evaluation methods and other aspects,And we have a further deep understanding of the diversity, science, fun and interactivity brought by the new generation of information technology in the field of teaching and learning.

The Impact of CALL Teacher Preparation Program on EFL Teachers' Integration of Technology

This research examines how Computer Assisted Language Learning (CALL) instructors are currently using technology in language instruction, the difficulties they encounter, how Computer Assisted Language Learning Teacher Preparation Program (CALLTPP) affects English Language Teachers (ELTs) professional development and technology integration and the implications of CALLTIT for teacher education programs. During a six-month blended learning program called CALLTPP, five CALL teachers engaged in online sessions and asynchronous communication to improve their comprehension and use of CALL in the classroom. Convenience sampling was used to choose the participants, who then submitted narratives summarizing their experiences. The narratives' thematic analysis showed that CALLTPPs had a favorable impact on ELTs' capacity for incorporating technology and professional development. Participants stated that they felt more competent and confident when incorporating technology, which increased learning outcomes and pupil engagement. On the other hand, difficulties including perceived complexity and a lack of formal CALL education were noted. In addition to highlighting the necessity of continuing support and training for CALL teachers, the study underscores the significance of digital literacy and pedagogical understanding for successful CALL integration.

Multilinguality and LLOD: A survey across linguistic description levels

Limited accessibility to language resources and technologies represents a challenge for the analysis, preservation, and documentation of natural languages other than English. Linguistic Linked (Open) Data (LLOD) holds the promise to ease the creation, linking, and reuse of multilingual linguistic data across distributed and heterogeneous resources. However, individual language resources and technologies accommodate or target different linguistic description levels, e.g., morphology, syntax, phonology, and pragmatics. In this comprehensive survey, the state-of-the-art of multilinguality and LLOD is being represented with a particular focus on linguistic description levels, identifying open challenges and gaps as well as proposing an ideal ecosystem for multilingual LLOD across description levels. This survey seeks to contribute an introductory text for newcomers to the field of multilingual LLOD, uncover gaps and challenges to be tackled by the LLOD community in reference to linguistic description levels, and present a solid basis for a future best practice of multilingual LLOD across description levels.

The influence of XBRL technology on the quality of financial reporting: exploring mediating variables in the Indian context

Purpose This study aims to examine the impact of extensible business reporting language (XBRL) technology and its functionality on various aspects of financial reporting and its overall quality. Design/methodology/approach To conduct this study, data was collected from a variety of professionals, including accountants, auditors, tax advisors and others. A structured research instrument was developed, and the collected data were analysed using structural equation modelling and mediation analysis techniques. Findings The study’s results showed that XBRL technology and its functionality have a noteworthy impact on different aspects of financial reporting. Moreover, the various aspects of financial reporting positively affect the overall quality of financial reporting. Research limitations/implications This study solely relied on the opinions of various professionals regarding the current issue under investigation and did not empirically assess the reporting practices of companies by examining their XBRL-based reports. Additionally, it concentrated solely on financial reporting aspects and did not account for non-financial aspects. The main theoretical contributions of this paper to technology in financial reporting, XBRL and accounting literature are that it sheds light on the influence of the use of technologies in the business reporting process and their influence on various aspects of business reporting, which has only received confined focus from earlier studies so far. Practical implications This study’s findings could provide valuable insights to the managerial teams of organizations seeking to digitize their business reporting practices, specifically in areas such as regulatory compliance, integrated reporting and timely dissemination of reports in a sustainable way. Furthermore, it could help these teams reap the benefits of technology for various regulatory compliance matters. Originality/value This study could assist business organizations and regulatory authorities in adopting and implementing technology such as XBRL for accounting and business reporting. Furthermore, the study’s findings can aid in enhancing financial reporting practices by considering emerging aspects such as ESG and sustainability aspects.

“Deep Learning Based Classification of Single-Hand South Indian Sign Language Gestures”

Gesture recognition is a branch of computer science and language technology dedicated to utilizing mathematical algorithms for the analysis of human gestures. Within the realm of non-verbal communication, the pivotal role of human arm movements and gestures remains a focal point. This research introduces advanced multi-stream deep transfer learning models tailored for identifying signs from South Indian languages, specifically Kannada, Tamil, and Telugu. The primary aim is to offer support to individuals encountering speech disorders or disabilities. The key deep transfer learning models utilized include Inception-V3, VGG-16, and ResNet-50, which have been modified and improved to attain heightened classification efficacy. The dataset comprises 35,000 images capturing single-hand gestures. In the realm of models, Inception-V3 exhibits the utmost test accuracy, standing at 91.45%, alongside a validation accuracy of 93.45% when tasked with the classification of single-hand gesture images across thirty-five distinct categories. The importance of this study lies in its prospective utility for creating an automated system that can support and improve the functional capabilities of individuals facing speech disorders or disabilities.

MDVC corpus: empowering Moroccan Darija speech recognition

Automatic speech recognition (ASR) technology has significantly transformed human-machine interactions, but it remains limited in its representation of diverse languages and dialects. Moroccan Darija, the lively Moroccan dialect, has long been underrepresented in the realm of language technology. To address this gap, we present a novel corpus of audio files accompanied by meticulously transcribed Moroccan Darija speech. The corpus comprises 1,000 hours of diverse content, featuring multiple Moroccan accents, extracted from 80 YouTube channels. To standardize the representation of Moroccan Darija in our corpus, we made efforts to establish consistent writing norms and conventions. In addition to the dataset creation, we applied fine-tuning using the Wav2Vec2 model on the Moroccan Darija voice corpus (MDVC) dataset achieving a remarkable word error rate (WER) of 9%. This article discusses the current state of Moroccan Darija research, highlighting the scarcity of resources and the need for robust ASR systems. Our contribution offers a valuable resource for researchers and developers, and by standardizing the Darija language, we strive to improve ASR system for this low resource language.

Supporting meta-model-based language evolution and rapid prototyping with automated grammar transformation

In model-driven engineering, developing a textual domain-specific language (DSL) involves constructing a meta-model, which defines an underlying abstract syntax, and a grammar, which defines the concrete syntax for the DSL. We consider a scenario in which the meta-model is manually maintained, which is common in various contexts, such as blended modeling, in which several concrete syntaxes co-exist in parallel. Language workbenches such as Xtext support such a scenario, but require the grammar to be manually co-evolved, which is laborious and error-prone. In this paper, we present GrammarTransformer, an approach for transforming generated grammars in the context of meta-model-based language evolution. To reduce the effort for language engineers during rapid prototyping and language evolution, it offers a catalog of configurable grammar transformation rules. Once configured, these rules can be automatically applied and re-applied after future evolution steps, greatly reducing redundant manual effort. In addition, some of the supported transformations can globally change the style of concrete syntax elements, further significantly reducing the effort for manual transformations. The grammar transformation rules were extracted from a comparison of generated and existing, expert-created grammars, based on seven available DSLs. An evaluation based on the seven languages shows GrammarTransformer’s ability to modify Xtext-generated grammars in a way that agrees with manual changes performed by an expert and to support language evolution in an efficient way, with only a minimal need to change existing configurations over time.

“Deep Learning Based Classification of Single-Hand South Indian Sign Language Gestures”

Gesture recognition is a branch of computer science and language technology dedicated to utilizing mathematical algorithms for the analysis of human gestures. Within the realm of non-verbal communication, the pivotal role of human arm movements and gestures remains a focal point. This research introduces advanced multi-stream deep transfer learning models tailored for identifying signs from South Indian languages, specifically Kannada, Tamil, and Telugu. The primary aim is to offer support to individuals encountering speech disorders or disabilities. The key deep transfer learning models utilized include Inception-V3, VGG-16, and ResNet-50, which have been modified and improved to attain heightened classification efficacy. The dataset comprises 35,000 images capturing single-hand gestures. In the realm of models, Inception-V3 exhibits the utmost test accuracy, standing at 91.45%, alongside a validation accuracy of 93.45% when tasked with the classification of single-hand gesture images across thirty-five distinct categories. The importance of this study lies in its prospective utility for creating an automated system that can support and improve the functional capabilities of individuals facing speech disorders or disabilities.

Building Text and Speech Benchmark Datasets and Models for Low‐Resourced East African Languages: Experiences and Lessons

ABSTRACTAfrica has over 2000 languages; however, those languages are not well represented in the existing natural language processing ecosystem. African languages lack essential digital resources to effectively engage in advancing language technologies. There is a need to generate high‐quality natural language processing resources for low‐resourced African languages. Obtaining high‐quality speech and text data is expensive and tedious because it can involve manual sourcing and verification of data sources. This paper discusses the process taken to curate and annotate text and speech datasets for five East African languages: Luganda, Runyankore‐Rukiga, Acholi, Lumasaba, and Swahili. We also present results obtained from baseline models for machine translation, topic modeling and classification, sentiment classification, and automatic speech recognition tasks. Finally, we discuss the experiences, challenges, and lessons learned in creating the text and speech datasets.

Development of a 2-D Motion Tracker Application Project Assisted By Computer Vision to Enhance Student’s Laboratory Skills

The lecture method that is often applied by the teacher in class can also cause the learning experience of students to not be optimal; as a result, students only understand the theory and concept of projectile motion. One method that can be used to learn physics is the experimental method. This study aims to produce a projectile motion tracker application project and describe the effect of applying this application project in the physics learning process, especially related to students' laboratory skills. The research method used is research and development (RD), using the 4-D development model (Define, Design, Develop, and Disseminate). Pre- and post-test results were obtained from 74 high school students to assess their laboratory skills before and after treatment. The instruments used were questionnaires and tests. The results show that the projectile motion tracker application project can be employed as a tool for physics education, based on expert test results with very good categories in language, visual appearance, and software engineering. In addition, based on the results of the practicality test for teachers and students, the test results were in the very practical category, respectively, from the aspects of ease of use, attractiveness, efficiency, and usefulness. The results of students' pre- and post-test on their laboratory skills show that the projectile motion tracker application project can enhance students' laboratory skills. The projectile motion tracker application project can be used as an alternative by teachers, lecturers, or education practitioners to support teaching and learning activities.

Freedom comes at a cost?: An exploratory study on affordances' impact on users' perception of a social robot.

Along with the development of speech and language technologies, the market for speech-enabled human-robot interactions (HRI) has grown in recent years. However, it is found that people feel their conversational interactions with such robots are far from satisfactory. One of the reasons is the habitability gap, where the usability of a speech-enabled agent drops when its flexibility increases. For social robots, such flexibility is reflected in the diverse choice of robots' appearances, sounds and behaviours, which shape a robot's 'affordance'. Whilst designers or users have enjoyed the freedom of constructing a social robot by integrating off-the-shelf technologies, such freedom comes at a potential cost: the users' perceptions and satisfaction. Designing appropriate affordances is essential for the quality of HRI. It is hypothesised that a social robot with aligned affordances could create an appropriate perception of the robot and increase users' satisfaction when speaking with it. Given that previous studies of affordance alignment mainly focus on one interface's characteristics and face-voice match, we aim to deepen our understanding of affordance alignment with a robot's behaviours and use cases. In particular, we investigate how a robot's affordances affect users' perceptions in different types of use cases. For this purpose, we conducted an exploratory experiment that included three different affordance settings (adult-like, child-like, and robot-like) and three use cases (informative, emotional, and hybrid). Participants were invited to talk to social robots in person. A mixed-methods approach was employed for quantitative and qualitative analysis of 156 interaction samples. The results show that static affordance (face and voice) has a statistically significant effect on the perceived warmth of the first impression; use cases affect people's perceptions more on perceived competence and warmth before and after interactions. In addition, it shows the importance of aligning static affordance with behavioural affordance. General design principles of behavioural affordances are proposed. We anticipate that our empirical evidence will provide a clearer guideline for speech-enabled social robots' affordance design. It will be a starting point for more sophisticated design guidelines. For example, personalised affordance design for individual or group users in different contexts.

Unlocking Insights: A Literature Review on Enhanced Confix Stripping and Nazief & Adriani Algorithm Modifications for Makassar Language Text Stemming

This study investigates the Nazief and Adriani Algorithm and the Enhanced Confix Stripping Stemmer (ECS) in the context of Makassar language. Following a comprehensive investigation, the Nazief & Adriani Algorithm demonstrates proficiency in capturing the complexities of Makassar language by applying numerous morphological criteria. Meanwhile, the Enhanced Confix Stripping Stemmer (ECS) exhibits versatility in dealing with language obstacles, identifying opportunities for further improvement. Using Sastrawi, Confix Stripping, Enhanced Confix Stripping, and Nazief-Adriani, the study emphasizes the need of using linguistically suitable techniques for exact analysis. This work sheds light on improving text processing technology in Makassar language, opening the path for algorithms customized to the language's unique qualities.

Students’ English Language Acquisition and their Proficiency Improvement at IPRC Kigali, Rwanda

This study entitled “Students’ English Language Acquisition and their Proficiency Improvement at IPRC Kigali, Rwanda”, was carried out to evaluate the effect of students’ English language acquisition on their proficiency improvement at IPRC Kigali, Rwanda. It had three (3) specific objectives which were to: assess strategies used for teaching English Language at IPRC Kigali, identify ways in which IPRC Kigali students use to learn English as a language of instruction, as well as identifying factors affecting English acquisition as the second language at IPRC Kigali. The methodology that was used in data collection is descriptive survey. Purposive and stratified random were used as sampling techniques. Questionnaires were used as data collection instruments. The sample size was determined by Slovin’s Formula as the population size is definite. 153 students were selected to answer to research questions. 93 of them responded and returned questionnaires. The data collected was analyzed using SPSS software. The study came up with the following findings: The mostly used strategies for the English Language Acquisition at IPRC Kigali include lecturers creating the learner-centered classroom to set students’ learning responsibilities (79.6%), and building in students’ motivation including value, self-efficacy and attributions (75.2%). The most ways in which IPRC Kigali students use to Learn English as a language of instruction include imitation (100%), repetition and practice to pronounce words and memorize them (100) conversations and discussions with colleagues and lecturers (76.4%), using English in different situations like in meetings, in shopping malls, ... and other extra-curricular activities (75.2%). It has also been revealed that age (76.3%), attitude towards English (67.8%), motivation (100%), first language proficiency (89.3%), technology (100%), and Intellectual capacity (86.0%) affect the Acquisition of English as a Second Language. It has been recommended that It is of paramount importance to enhance teaching of English as a subject but also keeping in mind that this is a Medium of Instruction in Rwanda. Lecturers should interlink this philosophy with methodologies and strategies that allow students to take lead in the teaching and learning process. Lecturers should consider the students’ individual differences to help them improve individually. Drama and play should also be integrated in teaching and learning/acquisition of English to improve students’ speaking skills through performance.