Language Understanding Research Articles

ANALYSIS OF TRANSLATION SHIFT IN BILINGUAL CHILDREN'S STORYBOOKS

lingual illustrated children's stories are excellent tools for teaching English to beginners, particularly children. Translating such stories requires the use of simple and easily understandable language to aid children's growth and development. Thus, it is crucial that translation shifts in children's stories effectively convey meaning and are suitable for young learners. This study focuses on the translation shifts in the illustrated bilingual children's story "The Mouse and the Frog," which is rich in moral values and character development.The urgency of this research lies in identifying and analyzing the types and forms of translation shifts in noun phrases within the story. The objective is to develop models and provide empirical evidence on these shifts, enhancing understanding of translation practices, particularly in the context of noun phrases. The research employs document analysis and data triangulation methods, with media sourced from a YouTube channel.Results indicate that translation shifts in noun phrases predominantly occur in the form of unit shifts. This study aims to contribute a new model to the field of English translation, improving and expanding knowledge in this area. The findings are expected to enhance readers' understanding of translation, particularly for beginners in English, and provide insights for translators working with children's literature.

Unmasking Media Bias and Religious Zionism’s Impeding Political Influence on the Israeli-Palestinian Conflict

Israeli media coverage of the Israeli-Palestinian conflict is often accused of bias that skews public discourse in ways favoring hardline Zionist policy goals. This study analyzes news articles from the Times of Israel, Israel Hayom, and Jerusalem Post covering October 2023 violence to understand how language and framing shape understandings and political dynamics. A critical discourse analysis of 34 selected news articles published between October 7-30, 2023 was conducted using the CDA framework to uncover underlying ideological elements and their potential effects. The findings illuminated how certain outlets strategically frame information to reinforce unilateral policy preferences over collaborative solutions. By normalizing disproportionate responses to acts of resistance and denying the humanity of those contesting Zionist territorial ambitions. The Times of Israel and Israel Hayom frame narratives to consolidate unilateral Zionist control and normalize militarized policies. Jerusalem Post aims to build a pro-Israel narrative through political language and ‘us vs. them’ framing. Strategic omissions deny Palestinian grievances and legitimacy, while emotive reporting justifies disproportionate force. The studied media construct narrow nationalist discourses that delegitimize Palestinian perspectives and fuel intractability. Reforms are needed to diversify sourcing and fact-check distortions to curb biases exacerbating political polarization rather than reconciliation.

Image to Label to Answer: An Efficient Framework for Enhanced Clinical Applications in Medical Visual Question Answering

Medical Visual Question Answering (Med-VQA) faces significant limitations in application development due to sparse and challenging data acquisition. Existing approaches focus on multi-modal learning to equip models with medical image inference and natural language understanding, but this worsens data scarcity in Med-VQA, hindering clinical application and advancement. This paper proposes the ITLTA framework for Med-VQA, designed based on field requirements. ITLTA combines multi-label learning of medical images with the language understanding and reasoning capabilities of large language models (LLMs) to achieve zero-shot learning, meeting natural language module needs without end-to-end training. This approach reduces deployment costs and training data requirements, allowing LLMs to function as flexible, plug-and-play modules. To enhance multi-label classification accuracy, the framework uses external medical image data for pretraining, integrated with a joint feature and label attention mechanism. This configuration ensures robust performance and applicability, even with limited data. Additionally, the framework clarifies the decision-making process for visual labels and question prompts, enhancing the interpretability of Med-VQA. Validated on the VQA-Med 2019 dataset, our method demonstrates superior effectiveness compared to existing methods, confirming its outstanding performance for enhanced clinical applications.

Analisis Kesulitan Belajar Dalam Mata Kuliah Analisis Kompleks Ditinjau Dari Kemandirian Belajar Mahasiswa

Tadris Mathematics students have difficulty understanding complex analysis courses when studying independently because many students still lack confidence in doing assignments and only rely on help from others. Learning difficulties greatly affect learning independence. This study aims to determine the level of difficulty in learning complex analysis based on students' learning independence. The research method used in this research is descriptive with a qualitative approach. The data collection technique used was purposive sampling by distributing questionnaires in the form of google forms given to 20 students of the Tadris Mathematics study program at UIN K.H Abdurrahman Wahid Pekalongan. Then the researcher conducted interviews with three students as representatives of classes A, B, C. The results of the study show that in general, Tadris Mathematics students have learning difficulties based on independence and difficulty understanding language in complex analysis courses. This learning independence can create students' ability to control and organize themselves in carrying out learning activities.

Chinglish as border languaging

Abstract In an era where migration across borders is increasingly the norm, how are our understandings of language and the ways we talk about language being reimagined along the way? This article examines this question by attending to the shifting metadiscourses of “Chinglish,” a colloquialism referring to Chinese-English hybridizations. Chinglish, originally used to describe an incompetent interlanguage, has come to be invoked as a means of establishing “China English” as a legitimate world English variety, or more recently even as an innovative form of translingual practice. This article presents Chinglish as a form of “border languaging,” which enables us to take stock of the shifting meanings of Chinglish in relation to the linguistic “border” between English and Chinese upon which such metadiscursive framings hinge, and how the shifting orientations to such linguistic borders invite new ways of conceptualizing Chinglish and historically marginalized language practices more generally.

Event extraction as machine reading comprehension with question-context bridging

Most existing methods regard event extraction as the classification task. They not only heavily rely on named entity recognition, causing error propagation, but are also inefficient in low resource scenarios. To deal with above challenges, we propose an improved machine reading comprehension (MRC) approach, namely MRCBEE. Firstly, a new paradigm is applied to redefine event extraction as MRC task by designing question templates for event detection and argument extraction tasks. Specially, Question-Context Bridging is a new graph structure drawn to reconstruct the semantic relationship between the template and the text, in order to strengthen the prompt role of question templates. Then, a cross-domain attention module is designed to integrate both the semantic feature and global feature of words. A new GNN based on gated mechanism is proposed to capture the global feature and filter the information of invalid neighbors. Finally, the results of our experiments show that MRCBEE achieves better performance than the state-of-the-art methods on ACE2005 and ERE datasets. And it outperforms prior methods in low resource and complex text scenarios.

Refining ChatGPT-Generated Code: Characterizing and Mitigating Code Quality Issues

Since its introduction in November 2022, ChatGPT has rapidly gained popularity due to its remarkable ability in language understanding and human-like responses. ChatGPT, based on GPT-3.5 architecture, has shown great promise for revolutionizing various research fields, including code generation. However, the reliability and quality of code generated by ChatGPT remain unexplored, raising concerns about potential risks associated with the widespread use of ChatGPT-driven code generation. In this article, we systematically study the quality of 4,066 ChatGPT-generated programs of code implemented in two popular programming languages, i.e., Java and Python, for 2,033 programming tasks. The goal of this work is threefold. First, we analyze the correctness of ChatGPT on code generation tasks and uncover the factors that influence its effectiveness, including task difficulty, programming language, time that tasks are introduced, and program size. Second, we identify and characterize potential issues with the quality of ChatGPT-generated code. Last, we provide insights into how these issues can be mitigated. Experiments highlight that out of 4,066 programs generated by ChatGPT, 2,756 programs are deemed correct, 1,082 programs provide wrong outputs, and 177 programs contain compilation or runtime errors. Additionally, we further analyze other characteristics of the generated code through static analysis tools, such as code style and maintainability, and find that 1,930 ChatGPT-generated code snippets suffer from maintainability issues. Subsequently, we investigate ChatGPT’s self-repairing ability and its interaction with static analysis tools to fix the errors uncovered in the previous step. Experiments suggest that ChatGPT can partially address these challenges, improving code quality by more than 20%, but there are still limitations and opportunities for improvement. Overall, our study provides valuable insights into the current limitations of ChatGPT and offers a roadmap for future research and development efforts to enhance the code generation capabilities of artificial intelligence models such as ChatGPT.

Rejuvenation of the Arabic Learning Paradigm: Considering Richard Rorty's Idea as an Alternative Education Discourse

The purpose of this study is to show the educational aspects of Richard Rorty's thought that are linked to Arabic language education. This research is a type of document analysis that identifies and selects data in the form of text, then checks, sorts and evaluates the core concepts of Rorty's thinking systematically to be able to understand and interpret it in the context of learning Arabic. The research method used is literature research. Data analysis is carried out through three stages, namely data reduction, data presentation and conclusion drawn/verification. The results of the study show that Rorty's Paradigm of Thinking touches on several important aspects about the purpose and method of language teaching. In Rorty's view, the function of language as a means of communication, Rorty emphasizes the role of language as a tool for social interaction and conveying meaning. In language education, Rorty's educational paradigm challenges an approach that focuses on teaching rigid and normative grammar. In addition, he emphasized the importance of education as a social construction process where students not only learn language, but also develop their perspective on the world and the way they interact with others. Thus, Rorty's thinking in relation to language education emphasizes creativity, social and cultural context, as well as a growing understanding of language and meaning.

APPOINTMENT MAKER USING COMPUTERIZED VOICE

The Appointment Maker Using Computerized Voice is a novel system designed to streamline the process of scheduling appointments in healthcare settings through the integration of voice recognition and natural language processing technologies. By leveraging speech-to-text and text-to-speech capabilities, the system enables users to interact with the appointment booking interface using spoken commands, eliminating the need for manual input and enhancing accessibility for individuals with diverse technological proficiencies. This intuitive interface simplifies the appointment scheduling process, allowing users to book, reschedule, or cancel appointments with ease. Furthermore, the Appointment Maker employs machine learning models to understand and interpret user queries, providing accurate responses and assistance in real-time. By leveraging pre-trained language models and fine-tuning them on domain-specific data, the system demonstrates robust performance in understanding diverse user inputs and generating contextually relevant responses. This capability enhances user experience by providing personalized and timely assistance, akin to interacting with a human receptionist.The Appointment Maker features a backend database for storing and managing appointment and user information, ensuring data integrity, security, and efficient resource utilization. The relational database schema facilitates seamless coordination between users, healthcare providers, and administrative staff, enabling efficient scheduling and management of appointments. Overall, the Appointment Maker Using Computerized Voice represents a significant advancement in healthcare administration, offering a user-friendly and efficient solution for appointment scheduling while improving accessibility and patient experience. Key Words: Computerized Voice ,Natural Language Understanding (NLU), Speech to Text, Text to Speech.

INTEGRATIVE STUDY OF LANGUAGE RULES IN THE PROCESS OF TEACHING JUNIOR SCHOOLCHILDREN THE AZERBAIJANI LANGUAGE

The morphology of the Azerbaijani language has a wide range, so the question arises about the introduction of grammatical and lexical system of language rules. The relevance of the study is determined by the need to update the means of teaching junior high school students the rules of the language in Azerbaijani language lessons. The article is designed to reveal the features of grammatical and lexical structure and a comprehensive study of the language norms of the Azerbaijani language for elementary school students. The article aimed to describe grammatical and lexical features and their expression during students’ integrative learning of language rules. For this, the research used the methods of analysis, synthesis, comparison, deduction, generalisation, and a pedagogical experiment. The deductive method played a crucial role in this study, revealing unique aspects of the child’s lexical and grammatical mastery. This was achieved by using common knowledge related to education and language capabilities. Deduction was important for the formulation of distinctive features and characteristics that should be taken into account by teachers when teaching the Azerbaijani language. In addition, the article uses the method of generalization to prepare exercises for schoolchildren from a wide range of educational subjects. This approach involved incorporating the theoretical ideas explored in the study into the content of the exercise. The aim was not only to introduce these concepts but also to reinforce their understanding of the tasks, thus assessing the degree of students’ understanding and mastery. This approach enriches the educational process, ensuring the effective implementation of theoretical knowledge in practical learning experience. As a result of the work, it was established that the comprehensive study of language rules from morphology involved increasing the level of student’s knowledge, namely their conscious understanding of words in the process of speaking. Therefore, the main areas of the formation of speaking abilities and skills of younger schoolchildren are revealed. The article proves that this process is closely related to the increase in vocabulary, which, in turn, is an indicator of the richness of the language. The study presents a structured approach to teaching Azerbaijani language to junior high school students, focusing on morphological analysis, suffix exercises, and the interrelation of grammatical and lexical features. It emphasizes integrating these aspects to enhance students’ understanding and use of language. Key findings demonstrate the effectiveness of this integrative method in improving vocabulary, speech culture, and overall linguistic competence.

Exploring the Correlation between English Proficiency and Reading Comprehension among Elementary Learners in the Philippines

This descriptive-correlational study aimed to determine the relationship between the English proficiency and reading comprehension of the Grade 6 learners at an elementary school in the province of Davao Occidental. Specifically, it aimed to determine the level of English proficiency of the respondents in terms of grammar and vocabulary and their level of reading comprehension. With the use of complete enumeration, there were 20 Grade 6 learners selected as respondents of the study. A validated questionnaire was used to assess the level of students’ English proficiency and the level of students’ reading. The data were analyzed using mean and Pearson-r correlation analysis. Results revealed that the level of English proficiency of the respondents is proficient. This implies that the learners show great understanding and application of the English language. Also, the level of reading comprehension of the respondents is described as high, indicating that they have strong understanding and reading comprehension abilities. Further, the result revealed that there is no significant relationship between the English proficiency and reading comprehension of the Grade 6 learners. It is recommended that further studies on other factors that affect the reading comprehension of elementary learners should be conducted.

Innovative Thinking Approach in Designing Teaching Materials for Ilm Badi Course: A Comprehensive Framework for Enhancing Students' Writing Skills

Students face several challenges in learning Ilm Badi, including difficulty understanding learning objectives, a lack of comprehension regarding the classification in Ilm Badi materials, insufficient visual stimuli in teaching materials, and challenges in generating new sentences. These issues indicate a mismatch between theoretical understanding and practical language skills. Therefore, this study aims to design teaching materials for Ilm Badi with an innovative thinking approach to stimulate students' writing creativity. The research method employed is Research and Development (R&D), integrating the instructional material design procedure by Sa'ud with the ADDIE model. Data were collected through interviews, observations, and questionnaires. The results indicate that the innovative thinking approach is relevant in strengthening creative thinking processes in general and supports the teaching of Ilm Badi with a more progressive and contextual approach. The teaching materials produced through detailed design stages involve composing content in Arabic and Indonesian, utilizing additional visualizations, and designing exercises to encourage students' innovative thinking abilities. The teaching materials design process includes collaboration with relevant experts. It undergoes expert assessments, resulting in teaching materials that meet standards for use in Ilm Badi learning, emphasizing innovative thinking. Further studies are necessary to test the designed teaching materials' effectiveness empirically.

Empowering customer satisfaction chatbot using deep learning and sentiment analysis

The rapid advancement of technology holds great promise for various types of users, clients, or service providers. Intelligent robots, whether virtual or physical, can simplify the reservation process. With the development of machines and processing tools, natural language processing (NLP) and natural language understanding (NLU) have emerged to help people comprehend spoken language through machines. In order to facilitate seamless human-machine interaction, we aim to address customer needs through a chatbot. The objective of this paper is to incorporate sentiment analysis techniques with deep learning algorithms to cater to customers’ needs during message exchanges. This study aims to create an intelligent chatbot to engage customers during their routine operations and offer support. In addition, it offers to companies a manner to detect sarcastic messages. The proposed chatbot utilizes deep learning techniques to predict users’ intentions based on the questions asked and provide a helpful and convenient answer. A new chatbot for the customer is presented to overcome with challenges related to a wrong statement like sarcastic one and feedback towards user messages. A comparison between deep and transfer learning gives a new insight to include sentiments and sarcasm detection in the conversion process.

THE IMPORTANCE OF STUDYING THE CONCEPT OF “SADOKAT” (LOYALTY) FROM A LINGUISTIC AND CULTURAL PERSPECTIVE

This article emphasizes the importance of studying the concept of "Sadokat" (loyalty) from a linguistic and cultural perspective. Originating from the Arabic language, "Sadokat" encompasses notions of loyalty, faithfulness, and devotion. This concept holdssignificant value not only in personal relationships but also in broader social and religious contexts. The article presents an analysis of "Sadokat" from a linguistic and cultural standpoint, illustrating its various forms and cultural expressions withinArabic culture. Studying this concept aids in deepening our understanding of language and cross-cultural communication.

THE TERM "CONCEPT" AS A BASIC CONCEPT OF COGNITIVE SEMANTICS

Today, there is much debate over the term cognitive linguistics, and there is no agreement on what exactly the term means. According to English linguists, cognitive linguistics is a branch of linguisticsIn cognitive semantics, the term "concept" refers to a mental representation of a category that encompasses a set of related objects, events, or ideas. Concepts are fundamental to human cognition and language understanding. Cognitive semantics emphasizes that the meaning of words and expressions is grounded in conceptual structures, which are shaped by our sensory-motor experiences and cultural context.

The Concept of Electronic Pensioner in Some Countries Versus Romania

We aim to introduce the concept of e-pensioner (EP) with the intention of having this term used as a document within the European Union’s strategy on Artificial Intelligence (AI) for the period 2023-2027 and after implemented into the legislation of member countries. The relationship between older generations and emerging forms of digitalization and artificial intelligence is the focal point, aiming to provide an easily approachable dimension for pensioners. This article presents an analytical framework and approaches for integrating pensioners into the digital era. Following the exposition of theoretical foundations, the article delineates the use of pensioner cards in select countries worldwide. The intersection of pensioners with the digital era proves to be a fertile entry point for examining regulatory ordinances in the digital realm. A novel digital form, the AI-based digital portal, involves a technique of facial authentication for pensioners to send digital life certificates. Two key factors are named across all examined countries. The primary driver of digital transformation is propelled by the desire to enhance the efficiency and efficacy of public administration, which becomes particularly crucial as populations age, leading to increased demand for social services and competition for limited resources in the developed public sector. The innovative application of technology presents opportunities for streamlining processes, enhancing productivity, and reducing costs while concurrently improving service quality. The use of artificial intelligence (AI) in the management and administration of public pension funds stands for a natural progression of technology in the public domain. In an environment characterized by complexity and volatility, AI can provide a level of analysis and prediction that exceeds human ability. Presently, there exists a wide array of AI-powered tools for object detection, natural language processing, understanding and inference, optical character recognition, predictive analytics, anomaly detection, noise elimination, model forecasting, recommendation systems, and monitoring and observability.

THE ROLE OF PRAGMATIC FACTORS IN THE SOCIALIZATION OF SPEECH

In the article, thoughts about the role of pragmatic factors and forms of manifestation in the social character of speech are expressed through examples. Pragmatic factors in the social character of speech are first of all manifested in intercultural communication.In the multifaceted process of intercultural communication, linguistic and cultural levels of mutual understanding between representatives of different language communities are distinguished, in other words, linguistic and extralinguistic. intercultural communication is a two-way process of foreign language and foreign cultural understanding, and in this regard, the act of speech implies interlinguistic and intercultural translation, the adequacy of which is at the level of pragmatic meaning of language units. It should be noted that sThe object of study of both sociolinguistics and pragmalinguistics is speech phenomena, which cannot be separated from each other in the analysis of speech processes. Both of these areas of linguistics have their own place in the social character of speech. That is why it is impossible to sharply separate them from each other.

From RNN techniques to pre-trained models: emphasis on the use in Arabic machine translation

<p>Neural Machine Translation (NMT) has gained increasing attention in recent years due to its promising performance over conventional approaches such as Statistical Machine Translation. Nevertheless, when applied to languages with different structures, such as the pair (English, Arabic) that interests us in this work, its efficiency is degraded.<br />Alternatively, the use of unsupervised pre-training of large neural models has recently made a significant leap forward in the field of Natural Language Processing (NLP). By warm starting from published checkpoints, NLP practitioners have been able to push the state-of-the-art boundaries on multiple benchmarks while simultaneously saving considerable computational time. The emphasis so far has been mainly on natural language understanding problems. In this paper, we show the efficacy of pre-trained checkpoints for Arabic Machine Translation. We have evolved a transformer-based sequence-to-sequence model which is compatible with pre-trained checkpoints publicly available for Arabic Bidirectional Encoder Representations from Transformers (AraBERT) and Arabic Generative Pre-trained Transformer (AraGPT) and conducted a thorough empirical study on the usefulness of initializing our Arabic MT model, both encoder and decoder, with these checkpoints. Our models yield new state-of-the-art results in Arabic MT.</p>

OncoGPT: Enhancing oncological decision-making with ASCO guideline–trained AI.

e13607 Background: General-purpose language models like ChatGPT, while proficient in a wide array of topics, often lack the specialized knowledge and precision required for specific fields such as oncology. The American Society of Clinical Oncology (ASCO) guidelines offer a comprehensive resource for oncology professionals, yet sifting through extensive documents for specific information can be daunting. To bridge this gap, we introduce OncoGPT, a specialized GPT model, tailored to provide precise and guideline-adherent oncological advice. Methods: We leveraged GPT-4's advanced language understanding capabilities as the foundation for OncoGPT. This customization involved an extensive training regimen using the complete text of the ASCO clinical guidelines. The training process was meticulously designed to ensure that OncoGPT not only assimilates the detailed information within these guidelines but also understands the context and intricacies of oncological care. Additionally, we created disease-specific versions of OncoGPT, each fine-tuned with guideline subsets pertinent to particular oncological conditions. Further, the training was augmented with specific prompts to foster kind, sincere, and respectful interactions, especially for patient-oriented responses. The model was designed to discern whether the user is a healthcare professional or a patient and adjust the complexity of its responses accordingly. For patients, additional emphasis was placed on delivering information in a kind, responsible, and empathetic manner. Results: OncoGPT's performance was rigorously evaluated for accuracy and reasoning against real-world clinical scenarios. The responses generated by the model were meticulously reviewed by an experienced oncologist to confirm adherence to ASCO guidelines and clinical relevance. This assessment revealed a high level of precision and contextual understanding in OncoGPT's outputs, demonstrating its potential as a valuable tool for oncology practitioners. Importantly, it showed a remarkable ability to tailor its communication style based on the user's background, providing simplified explanations for patients and more detailed responses for professionals. An example of a specific OncoGPT: Breast Cancer OncoGPT: https://chat.openai.com/g/g-VcuECT3jg-oncogpt-breast-cancer Conclusions: OncoGPT represents a significant advancement in the application of AI for medical decision-making. By integrating the depth of ASCO guidelines into a responsive and accurate AI model, OncoGPT offers oncology professionals a powerful tool for accessible, guideline-compliant advice. This development marks a crucial step towards integrating AI more profoundly into specialized medical fields, enhancing both the efficiency and accuracy of clinical decision-making.

AURA-The Privacy Preserving Multifunctional Desktop Voice Assistant

Abstract: The “AURA” is the project which proposes the development of the versatile desktop voice assistant which mainly focuses on the privacy of the user.The convenience of voice interaction has surged in popularity, but concerns about user privacy remain. Conventional cloud-based voice assistants raise anxieties regarding data collection, storage, and potential misuse. This survey paper explores the burgeoning field of privacy-preserving desktop voice assistants powered by Artificial Intelligence (AI). We delve into the core challenges of on-device speech recognition, natural language understanding, and response generation while maintaining user privacy. We conclude by outlining open research questions and future directions for this evolving field, paving the way for the development of secure and user-centric voice interaction experiences on desktops.