Language Translation Research Articles

Indonesian to Bengkulu Malay Statistical Machine Translation System

Machine translation is an automatic tool that can process language translation from one language to another. This research focuses on developing Statistical Machine Translation (SMT) from Indonesian to Bengkulu Malay and evaluating the quality of the machine translation output. The training and testing data consist of parallel corpora taken from Bengkulu Malay dictionaries and online resources for Indonesian corpora, with a total of 5261 parallel sentence pairs. Several steps are performed in SMT. The initial step is preprocessing, aimed at preparing the parallel corpus. After that, a training phase is conducted, where the parallel corpus is processed to build language and translation models. Subsequently, a testing phase is carried out, followed by an evaluation phase. Based on the research results, various factors influence the quality of SMT translation output. The most important factor is the quantity and quality of the parallel corpus used as the foundation for developing translation and language models. The machine translation output is automatically evaluated using the Bilingual Evaluation Understudy (BLEU), indicating accuracy values observed when using 500 sentences, 1500 sentences, 2500 sentences, 4000 sentences, and 5261 sentences are 80.56%, 90.86%, 92.50%, 92.91%, and 94.48% respectively.

ЯППИ – универсальный язык программирования миссий АНПА

This article proposes a universal language for developing autonomous underwater vehicle (AUV) missions. The main features of the proposed language are: the presence of high-level commands to describe the main tasks of underwater research, the use of commands in Russian, the readability of the created missions and their understandability for personnel involved in the use of underwater robotics, the lack of strict typing and parameterization of commands, the possibility of expanding the language by creating new commands based on the old ones. The article describes the main parameters of the language, syntax, semantics and basic commands, as well as language tools - a parameter refinement module, which interactively allows you to resolve uncertainty arising due to non-strict parameterization, low-level command module that implements more complex commands based on basic ones, a language extension module responsible for adding new commands, and a translator. The language translator allows you to translate created missions into task program codes for various AUVs. The created language is called URPL – Underwater Research Programming Language and at the current stage of research it is being tested and debugged on the AUVs of the IPMT FEB RAS, taking into account the experience accumulated at the institute.

Efficient incremental training using a novel NMT-SMT hybrid framework for translation of low-resource languages.

The data-hungry statistical machine translation (SMT) and neural machine translation (NMT) models offer state-of-the-art results for languages with abundant data resources. However, extensive research is imperative to make these models perform equally well for low-resource languages. This paper proposes a novel approach to integrate the best features of the NMT and SMT systems for improved translation performance of low-resource English-Tamil language pair. The suboptimal NMT model trained with the small parallel corpus translates the monolingual corpus and selects only the best translations, to retrain itself in the next iteration. The proposed method employs the SMT phrase-pair table to determine the best translations, based on the maximum match between the words of the phrase-pair dictionary and each of the individual translations. This repeating cycle of translation and retraining generates a large quasi-parallel corpus, thus making the NMT model more powerful. SMT-integrated incremental training demonstrates a substantial difference in translation performance as compared to the existing approaches for incremental training. The model is strengthened further by adopting a beam search decoding strategy to produce k best possible translations for each input sentence. Empirical findings prove that the proposed model with BLEU scores of 19.56 and 23.49 outperforms the baseline NMT with scores 11.06 and 17.06 for Eng-to-Tam and Tam-to-Eng translations, respectively. METEOR score evaluation further corroborates these results, proving the supremacy of the proposed model.

Machine Translation and NLP Tools: Developing and Refining Language Technologies for African Languages

Machine Translation (MT) and Natural Language Processing (NLP) tools have significantly evolved over the past few decades, leading to notable advancements in the field of language technology. However, most of these developments have been concentrated on languages with abundant data, such as English, Chinese, and European languages. African languages, with their unique linguistic structures and vocabularies, remain underrepresented in the MT and NLP landscape (Joshi et al., 2020). This article explores the challenges and opportunities in creating and refining machine translation models and NLP tools tailored to the unique structures of African languages. It discusses the specific linguistic features that set African languages apart, reviews the current state of MT and NLP tools for these languages, and outlines strategies for developing more effective models that cater to the diverse linguistic landscape of Africa.

The influence of complete and partial shared translation in the first language on semantic processing in the second language.

The influence of complete and partial shared translation in the first language on semantic processing in the second language.

Dexmedetomidine for agitation in dementia: Current data and future direction.

The incidence and prevalence of dementia, and thus dementia-related behavioral and psychological symptoms, are increasing significantly. Currently, there are limited safe and efficacious options for treating these symptoms. Dexmedetomidine has been used for agitation related to delirium and showed significant benefit in prior studies. This raises the question whether dexmedetomidine could also provide a safe and effective treatment for BPSD, including agitation related to dementia. Our team searched PubMed, Cochrane Database, and Ovid with the terms dexmedetomidine and dementia. Only studies published in English language journals, or with official English language translations, and human studies were included. All reports of dexmedetomidine for dementia were included regardless of study type. No completed studies on dexmedetomidine for agitation in dementia were identified. The TRANQUILITY study is in progress, although results are yet to be published. Dexmedetomidine has shown benefit for hospital delirium and for agitation in schizophrenia and bipolar disorder. However, there are no completed studies published on dexmedetomidine for agitation in dementia. Controlled studies with larger sample sizes are needed to assess the efficacy, safety, and the best route of administration for this drug in managing BPSD including agitation.

A Multi-Lingual Conversational AI Chatbot for Effective Educational Consultations: A Study of ACE-DS, University of Rwanda

The demand for real-time consultation services from organizations is increasing, leading to prolonged waiting times, primarily due to limited opportunities for face-to-face interactions and language barriers. This study addresses this challenge by leveraging Artificial Intelligence (AI), Natural Language Processing (NLP), and linguistic technologies to develop a multilingual conversational AI Chabot for managing educational consultation services, using the African Center of Excellence in Data Science (ACE-DS), University of Rwanda, as a case study. Information and frequently asked questions (FAQs) about ACE-DS were used to train a Deep Learning Gated Recurrent Units (GRUs) algorithm to power the Chabot. Language detection and translation APIs were integrated to facilitate seamless multilingual conversations. The result of user survey conducted revealed that over 60% of respondents expressed high satisfaction with the Chabot’s performance including grammar, efficiency, language preferences, and response quality. This study showcases the potential of AI particularly NLP in enhancing educational consultation services, providing a framework for efficient information acquisition.

Initial exploration into sarcasm and irony through machine translation

In this paper, we investigate sarcasm and irony as seen through a novel perspective of machine translation. We employ various techniques for translation, comparing both manually and automatically translated datasets of irony and sarcasm. We first clarify the definitions of irony and sarcasm and present an exhaustive field review of studies on irony both from purely linguistic as well as computational linguistic perspectives. We also propose a novel evaluation metric for the purpose of evaluating translations of figurative language, with a focus on machine-translated irony and sarcasm. The constructed English and Chinese parallel dataset includes polarized content from tweets as well as forum posts, categorized by irony types. The preferred translation model, mBART-50, is identified through a thorough experimental process. Optimal translation settings and the best-finetuned model for irony are explored, with the most effective model being finetuned on both ironic and non-ironic data. We also experimented which types of irony are best suitable for training in this specific task — short microblogging messages or longer forum posts. Moreover, we compare the capabilities of a well fine-tuned mBART to a prompt-based method using the recently popular ChatGPT model, with the conclusion that the former still outperforms the latter, although ChatGPT without any training can be considered as a “good enough” ad hoc solution in the case of a lack of data for training. Finally, we verify if the translated data – either manually, or with an MT model – can be used as training data in a task of irony detection. We believe that the presented research can be expanded into languages other than the presented here Chinese and English, which together with the ability to detect various categories of irony, could contribute to deepening the understanding of figurative language, especially irony and sarcasm.

Artificial Intelligence for Language Translation

Artificial Intelligence for Language Translation

Human-Machine Interaction Translation under Artificial Intelligence and Big Data: Analysis from the Perspective of Text Stratification and Corpus Construction

With the continuous advancement of artificial intelligence and big data technologies, the neural network-based machine translation driven by deep learning continues to flourish. This ongoing progress not only propels the application of translation technology and reshapes the translation industry but also profoundly impacts the realms of language learning and translation. This paper, situated against the backdrop of artificial intelligence and big data, focuses on the fundamental aspects of text stratification and corpus construction. Building upon discussions about text stratification and corpus construction, this paper extensively examines how human and machine interaction can effectively balance translation quality and cost, maximizing translation efficiency. Additionally, this paper proposes several innovation suggestions regarding future bilingual translation pedagogy.

AI-Enhanced Cross-Cultural Competence in STEM Education

Cross-cultural competence (CCC) is leaders' most critical skill set in science, technology, engineering, and math (STEM) education because it can help them effectively manage cultural diversity. The research provides insights into the potential of AI to foster more inclusive and culturally aware educational environments for STEM students using a theory that combines Constructivism and Cultural-Historical Activity Theory (CHAT). The study discusses two research questions: (1) How can AI-driven tools be used in STEM education to enhance cross-cultural sensitivity and communication? (2) What is the impact of AI-facilitated reflective practices on students' self-awareness of cultural biases? This research shows how AI-based language translators and cultural simulations enable learners to adapt to different cultures. According to Constructivism, learning occurs when learners actively construct their knowledge from experiences towards cultural sensitivity, understanding, and respecting other cultures while reducing stereotypes. CHAT, on its part, views AI as aligning with AI as one of the artifacts or tools that are at the disposal of human beings in order to facilitate social interaction; thus, it has the potential to enhance intercultural communication where verbal cues are not only linguistic but also non-verbal. A total of 228 respondents were reached through surveys, resulting in preliminary results showing improved CCC due to accommodative devices such as culturally-specific software simulations or language translators employing AI models. This study adds value to the existing literature by presenting empirical evidence of how AI can foster CCC in STEM education. It highlights the imperative of AI's theoretical and practical applications in educational curricula.

Artificial Intelligence (AI) Challenges and Opportunities in Translation : An African Experience

The Artificial Intelligence (AI) revolution has become a reality in today’s world and its importance for linguistics was recognized very early. Despite its unprecedent surge and integration into various academic fields including language teaching and translation, surprisingly, little work has been done by scholars in advancing discussions on the profound impact of the AI on the diversity of widely available languages in both developed and developing world. Africa is linguistically diverse continent with about one third of the world’s languages that are vastly underrepresented in the global digital data pool. AI translation machine is supported in only 25 languages out of over 2000 languages in the continent. The paper deploys homomorphism model of AI theory to interrogate the natural language data drawn the African languages to present the current and future challenges, opportunities and potential for developing AI algorithms that could fit neatly into the translation of the African languages. Most of the discussions in the paper focuses on the seven patterns of the AI, the usage and implementation of AI algorithms in the translation science. The research findings show some of the complexities of the African languages in which their syntactic categories have multiple corresponding semantic objects. Unlike English, the findings also reveal that syntactic operation in the African languages do not always have one corresponding semantic operation as postulated by the homomorphism model of AI theory. the study contributes to scholarly literature by stressing the limits and opportunities that relate to using AI in translation science and supplying input from NLP algorithms practitioners to expand the AI applicability operation in the translation science.

NATURAL LANGUAGE PROCESSING ALGORITHMS AND PERFORMANCE COMPARISON

Natural language processing (NLP) is the general name for the methods and algorithms developed for computers to understand, interpret and produce human language. NLP plays a critical role in many fields, from social media analyses to customer service, from language translation to healthcare. This paper provides a comprehensive overview of the basic concepts of NLP, popular algorithms and models, performance comparisons, and various application areas. Key concepts of NLP include language models, tokenisation, lemmatisation, stemming, POS tagging, NER and syntactic parsing. These concepts are critical for processing, analysing and making sense of texts. Language models include popular methods such as N-gram, Word2Vec, GloVe and BERT. NLP algorithms are classified as rule-based methods, machine learning methods and deep learning methods. Rule-based methods are based on grammatical rules, while machine learning methods work on the principle of learning from data. Deep learning methods, on the other hand, achieve high accuracy results by using large datasets and powerful computational resources. In the performance comparison section, it is stated that the algorithms are evaluated with metrics such as accuracy, precision, recall and F1 score. Advanced models such as BERT and GPT-3 show superior performance in many NLP tasks. In conclusion, the field of NLP is rapidly evolving, with significant advancements anticipated in several key areas. These include the creation of more effective and efficient models, efforts to reduce biases, enhanced privacy protection, the growth of multilingual and cross-cultural models, and the development of explainable artificial intelligence techniques. This paper provides a comprehensive overview to understand the current status and future directions of NLP technologies.

PERANCANGAN PENILAIAN KINERJA PERUSAHAAN MENGGUNAKAN BALANCED SCORECARD BERBASIS DIGITAL PADA PERUSAHAAN TEKSTIL DI CIREBON

Hanjani Group is a company related to the textile industry. In the performance evaluation process, currently there is no system that can enter all sales data at each branch of the company. The proposed solution is creating a website using the balanced scorecard method that allows the evaluation process to be presented with practical nominal results in performance assessment when viewed from four perspectives, there are finance, customers, internal business processes, and growth. This website provides a better view that managers will use to conduct statistical processes and compare systems from various branches of the company because it allows for integration or connection between disciplines. Elements of system analysis and design are explained using context diagrams, flowcharts, DFDs, and ERDs. The steps taken by designing a software system using PHP (metatext preprocessor) as the translation language and MySQL as the database manager. After testing by the company owner, this website can load performance evaluation reports at various branches of the company in Hanjani Group. Key words: Performance Assessment, Balanced Scorecard, Hanjani Group

Movie Titles’ Translation Strategies from English into Thai in Monomax Application

The objective of this study is to analyze the strategies employed in translating English movie titles into Thai within the Monomax Application. A total of 501 English movie titles spanning the decade of 2011 and 2020 were examined because a decade provides clear guidelines for data collection, analysis, and interpretation. The strategies for translating film titles into Thai conducted by Thongwan (2012) served as a framework for this study. The findings revealed that all 10 translation strategies were employed as follows respectively: 1) naming a new name regardless of the old name, 2) naming a new name based on the original name, 3) partial translation and adding Thai language, 4) partial transliteration and adding Thai language, 5) all translation and adding Thai language, 6) all transliteration and adding Thai language, 7) all transliteration without adding Thai, and 8) all translation without adding Thai language. Meanwhile, the last two which are 9) partial transliteration without adding the Thai language and partial translation without adding the Thai language were found used only once per each. Notably, the translated movie titles in Thai often featured distinctive and attractive elements, such as rhyme and alliteration. These findings corresponded to Nida & Taber (1974) translation theory that translation process is related to purpose and cultural linguistic norms. Future studies could explore the reception and perception of these translated titles among Thai audiences, shedding light on which strategies resonate most effectively and why.

Maximal Genetic Code Symmetry Is a Physicochemical Purine-Pyrimidine Symmetry Language for Transcription and Translation in the Flow of Genetic Information from DNA to Proteins.

Until now, research has not taken into consideration the physicochemical purine-pyrimidine symmetries of the genetic code in the transcription and translation processes of proteinogenesis. Our Supersymmetry Genetic Code table, developed in 2022, is common and unique for all RNA and DNA living species. Its basic structure is a purine-pyrimidine symmetry net with double mirror symmetry. Accordingly, the symmetry of the genetic code directly shows its organisation based on the principle of nucleotide Watson-Crick and codon-anticodon pairing. The maximal purine-pyrimidine symmetries of codons show that each codon has a strictly defined and unchangeable position within the genetic code. We discovered that the physicochemical symmetries of the genetic code play a fundamental role in recognising and differentiating codons from mRNA and the anticodon tRNA and aminoacyl-tRNA synthetases in the transcription and translation processes. These symmetries also support the wobble hypothesis with non-Watson-Crick pairing interactions between the translation process from mRNA to tRNA. The Supersymmetry Genetic Code table shows a specific arrangement of the second base of codons, according to which it is possible that an anticodon from tRNA recognises whether a codon from mRNA belongs to an amino acid with two or four codons, which is very important in the purposeful use of the wobble pairing process. Therefore, we show that canonical and wobble pairings essentially do not lead to misreading and errors during translation, and we point out the role of physicochemical purine-pyrimidine symmetries in decreasing disorder according to error minimisation and preserving the integrity of biological processes during proteinogenesis.

Validation of the Spanish Version of the Breakthrough Pain Assessment Tool in Patients With Cancer.

Assessment of breakthrough pain is essential for adequate management of cancer pain. The Breakthrough Pain Assessment Tool (BAT) has been proven to be a brief, multidimensional, and reliable questionnaire for the assessment of breakthrough cancer pain (BTCP). Currently, there are no validated instruments in Spanish that allow assessing BTCP. The objective of this study was to validate the Breakthrough Pain Assessment Tool - Spanish (BAT-S) version of the BAT in adult patients with cancer. The BAT-S was tested in a prospective observational study conducted in adult patients with cancer-related pain and treated in a comprehensive cancer center in Mexico. We conducted a forward-backward translation and cross-cultural equivalence test in the Spanish language. The psychometric properties in patients with cancer were assessed using factor analysis, reliability, and validity. To assess reliability, the Kappa test and the intra-class correlation coefficient were used. For consistency, Cronbach's alpha test was used. Seventy patients participated in the study; 140 questionnaires were analyzed. The Spanish translation was well accepted by participants. Reliability was comprised between 0.746 for "use of analgesics" and 1.00 for "pain location." Thirteen of the 14 items had values above 0.8, and 12 above 0.9. Cronbach´s alpha coefficient was 0.7. This study confirms that BAT-S is a valid and reliable questionnaire to assess breakthrough pain in Mexican patients with cancer. This newly validated tool may be used to facilitate clinical management of primarily Spanish-speaking patients with breakthrough cancer pain. This study describes a prospective observational study to assess the validity and reliability of the Breakthrough Pain Assessment Tool in its Spanish version. The results support the use of this newly validated tool to facilitate clinical management of primarily Spanish-speaking patients with breakthrough cancer pain.

Reinforcement of low-resource language translation with neural machine translation and backtranslation synergies

<p><span lang="EN-US">This research investigates challenges and advancements in neural machine translation (NMT), specifically targeting English-to-Kannada translation. Emphasizing the scarcity of data and linguistic complexity in low-resource languages (LRL), particularly Kannada, the study underscores the need for specialized techniques. Starting with exploration of Kannada's historical and cultural significance, the paper highlights critical importance of linguistic comprehension. The primary objective is to develop robust NMT models for precise and contextually relevant translations in low-resource scenarios. The novelty of this research lies in its innovative approach to Kannada NMT challenges, incorporating comprehensive examination of historical and cultural context to establish strong linguistic foundation. Motivated by the urgency to address translation needs in LRL, the paper proposes novel strategies, advocating notably for backtranslation to generate synthetic parallel corpora. Rigorous testing, including bilingual evaluation understudy (BLEU) score assessments, evaluates effectiveness of these proposed approaches. Beyond assessing backtranslation, the study explores challenges faced by Kannada NMT in handling dialectical and spelling variations. The research reports substantial 83-percentage-point average increase in BLEU scores, contingent on aligning unique Kannada terms with the same domain as existing occurrences. This study contributes significantly to Kannada natural language processing by offering novel insights into NMT intricacies and providing practical solutions for enhancing translation accuracy in low-resource settings.</span></p>

Assessment of printed lung cancer surgery patient education materials in the United States

ObjectiveWell-designed patient education materials (PEMs) increase health literacy, which has been linked to better surgical patient outcomes. The quality of lung cancer surgery PEMs is unknown, however. Here we assessed printed lung cancer surgery PEMs for readability, understandability, actionability, and accessibility. MethodsVarious lung cancer programs throughout the United States were contacted for their lung cancer surgery PEMs. The readability of the received materials was calculated using 6 readability tests. Four thoracic surgeon–advanced practice practitioner dyads scored the PEMs for understandability, actionability, and accessibility using the Patient Education Material Assessment Tool and the Accessibility Assessment Tool, with the recommended minimum threshold of 70%. One-sample t tests were performed to compare each parameter against its recommended threshold. ResultsOut of 34 institutions contacted, 18 (52.9%) provided PEMs. The average reading level of the PEMs ranged from 7th grade to 11th grade, significantly exceeding the recommended 6th grade health literacy threshold (P < .01). Although mean understandability (73.7 ± 13.2%) and actionability (70.2 ± 17.8%) scores were not significantly different from the minimum threshold, and the mean accessibility score (81.8 ± 13.5%) was significantly higher than the threshold (P < .05), there was wide variation in the scores. Most PEMs scored well in organization and writing but lacked other features that can enhance patient understanding, such as visual aids and summaries. ConclusionsPEMs are written at reading levels that are too advanced for patients. Although PEMs scored well in understandability, actionability, and accessibility, analysis of individual items revealed the need for improvement, including the use of shorter sentences, more visual aids and summaries, and expansion of language translations.

Improving End-to-End Sign Language Translation With Adaptive Video Representation Enhanced Transformer

Improving End-to-End Sign Language Translation With Adaptive Video Representation Enhanced Transformer