Machine Translation Research Articles

Revisiting translator competence in the age of artificial intelligence: the case of legal and institutional translation

ABSTRACT Multi-componential models of translation competence are widely used in translator training as a yardstick for curricular and syllabus design. These models must be adapted to reflect professional trends, such as the impact of artificial intelligence, and machine translation in particular, on working methods. This paper describes the process of adapting a pioneering model of legal translation competence to the broader scope of institutional translation in light of recent trends, as verified by triangulating information from multiple interviews, analyses of translation volumes and job descriptors and other professional inputs. The resulting revised descriptor was validated through a survey of 474 translation professionals from 24 international organisations of diverse sizes and domain specialisations. The suitability of the descriptor was corroborated across the board, but variations were found in perceptions of the relevance of sub-competences to ensure translation quality. Profiles with a stronger specialisation in legal translation or more experience in institutional translation showed higher awareness of the relevance of all the sub-competences, especially the core language, strategic and thematic competences, and even more so for translating texts of a legal or administrative nature. The implications of these findings for training purposes in particular are discussed.

Reading against all odds: creative coping strategies of international students in HE

ABSTRACT Many overseas students in Higher Education in the UK struggle to understand the compulsory texts for their course, and obtain lower scores for their modules than their monolingual peers. While the existence of this achievement gap is well established in the literature, little is known about the ways in which overseas students in HE with limited English language and literacy skills meet the challenges posed by the compulsory reading on their course. The current study is novel because we focus on a postgraduate Law module and use a mixed methods approach to study the relationship between students’ language and literacy profiles and their academic achievement. The results show that students’ vocabulary sizes did not provide enough lexical coverage for them to understand the set texts and that the language and literacy profiles of our students did not correlate with their module scores. In-depth interviews revealed that students with IELTS scores of 6.0 or lower used ‘creative reading’ strategies such as machine translation, to pass assignments, while staff used ‘compassionate marking’ strategies in the case of struggling writers, which means module scores for some students were likely inflated. Implications for admissions tutors and teachers in HE are formulated at the end.

A quantitative evaluation of online translators using Hindi web queries

Understanding machine translation (MT) quality is becoming more and more important as MT usage continues to rise in the translation industry. The acceptance of MT output based on their performance and, ultimately, how acceptable the translators actually are, have received relatively less attention so far. MT plays a vital role in CLIR systems and their retrieval efficiency is directly proportional to the translation accuracy of the queries. The varied meanings of words, sentences carrying multiple interpretations, and differing grammatical structures across languages contribute to the complexity of the MT task. The lack of structural constraints and the presence of ambiguity further compound the complications especially in case of web queries. The objective of this work is to assess the accuracy of free online translators in translating Hindi web queries. The accuracy of the translators has been evaluated on various metrics, i.e., BLEU, NIST, METEOR, hLepor, CHRF and GLEU. Our findings indicate that the translation accuracy for longer queries is higher than the shorter ones. Overall Google translator’s performance has been found the best while Systran performs the worst with 42.06% performance difference between the two. The present work intends to help researchers in further evaluating and analyzing the MT systems specially in context of web query translation, ultimately leading to improved translation quality and retrieval accuracy in CLIR.

Sanskrit to Hindi language translation using multimodal neural machine translation

Machine translation (MT) is a subfield of computer features that focuses on the automatic translation from one natural language into another without any human involvement. Due to native people interacting in a variety of languages, there is a great need for translating information between languages to send and communicate thoughts. However, they disregard the significance of semantic data encoded in the text features. In this paper, multimodal neural machine translation (MNMT) is proposed for Sanskrit-Hindi translation. The main goal of the proposed method is to fully utilize semantic text features on NMT architecture and to minimize testing and training time. The MNMT is validated on two different NMT architectures: recurrent neural network (RNN) and self-attention network (SAN). The MNMT method’s efficacy is demonstrated by employing the dataset of Sanskrit-Hindi Corpora. Extensive experimental outcomes represent the proposed method’s enhancement over baselines on both architectures. The existing methods, namely, English-to-Indian MT system, Sanskrit-Hindi MT system, and hybrid MT system are used to justify the efficacy of the MNMT method. When compared to the above-mentioned existing methods, RA-RNN respectively achieves a superior BLEU and METEOR of 80.5% and 75.3%, while the RA-SAN respectively achieves a superior BLEU and METEOR of 78.2% and 77.1%.

Advancements in Neural Machine Translation: Methodological Innovations and Empirical Insights for Cross-Linguistic Discourse Preservation

Abstract: Natural Language Processing (NLP), in the form recognizable today, really began to take hold in the 1980s, when machine learning helped propel it to soaring heights. However, due to a lack of processing power, machine learning, and to an extent, NLP, started slowing down in innovation and ideas and had almost ground to a relative halt, until the last decade, when a sudden increase in both productivity and interest in the machine learning helped increase the amount of knowledge in the space itself. This review provides several different case studies using different methodologies. The first paper was a deep analysis on how researchers were able to use Tesseract and Google Vision in tandem with automatic data mining methods to enrich the Cherokee language database in order to preserve it from extinction. The second paper takes a query translation-based approach toward translating English to Indian languages and utilizes a Multilingual Cross-Language Information Retrieval (MLCIR) system with tools such as Part of Speech Tagger (POST), Stop-Word, and Porter Stemmer. The third paper presents CoVe, which transfers knowledge from machine translation to improve performance on NLP tasks like sentiment analysis and question answering by using contextualized word vectors along with word embeddings, achieving new state-of-the-art results on some datasets. The fourth paper aims to translate English to Pakistan Sign Language (PSL) and also uses POST and goes through dependency analysis, sentence classification, and PSL using PLS trees. The fifth paper uses a Multilingual Neural Machine Translation (NMT) system for LowResource languages and incorporates two main models: a recurrent NMT and a transformer NMT. The sixth paper analyzes how a fine-tuned transformer model seems to work better than transformer models trained from scratch on high-resource languages, while vice-versa seems to occur for low-resource languages. The seventh paper adds to this by talking about how multilingual translation seems to work better than a back-translation model. Given the diverse array of approaches that could be used, we aim to identify the most efficient and correct methodology for future researchers to use in their work, based on the papers in this literature review.

Computational linguistics at the crossroads: A comprehensive review of NLP advancements

New NLP breakthroughs have put Computational Linguistics at a crossroads. NLP's past, present, and future are covered. This review explains computational linguistics' creation with a brief history of linguistics and computer science. Early solutions processed and understood natural language using rule-based systems using manually constructed linguistic rules. Over time, these tactics became increasingly problematic as language became more complex and obscure. Statistical approaches transformed operations. Neural network-based machine learning methods are leading the area because they can learn complicated patterns and representations from large text collections. A data-driven model revolution in natural language processing enhanced language modelling, machine translation, and sentiment analysis. Next, NLP improvements for several tasks and applications are evaluated. Language understanding models that capture semantic nuances and contextual relationships use deep learning frameworks. Word embeddings and transformer-based architectures like GPT and BERT perform well on benchmark datasets for text classification, question answering, and named item identification. The paper also shows how NLP interacts with computer vision, voice processing, and other domains to show the merits and cons of cross-disciplinary research. Multimodal techniques that combine text, graphics, and audio may increase natural language processing and interpretation. The review discusses NLP's effects on prejudice, justice, and privacy. Responsible development and implementation are needed when NLP technology becomes widespread due to algorithmic bias and data privacy concerns. NLP research directions and concerns are reviewed. Existing models may meet standards but fail in practice.

Analysis of Address Terms Translation Strategies: The Norms in the Translation of Address Terms in Moment in Peking

This paper, framed within Chestermans translation ethics, focuses on the translation of address terms in the first volume of Moment in Peking. It analyzes the practical application of professional norms and expected norms in the translation of novels, exploring the strategies of address terms translation. Through comparative analysis of different Chinese translations of address terms in the first volume of Moment in Peking (translated by Zhang Zhenyu, Yu Fei, and machine), this paper aims to reveal the ethical and professional norms that translators should consider in the selection of address terms and explore the performance of machine translation in terms of norms. The study finds that the ethical principles of human translators result in higher quality and reliability of translations, while machine translation, although formally faithful, still has deficiencies in context perception, emotional discernment, and target reader cognition.

An optimal framework for natural English translate processing: On the application of artificial neural network and Adam optimization algorithm

Traditional rule-based and statistical methods have limitations when dealing with complex language structures and semantics. In neural network machine translation algorithms, the objective function is usually to improve the accuracy of n-ary words. However, this does not guarantee a more natural and accurate translation. To overcome these challenges, this paper proposes an optimization algorithm for English natural translation processing based on neural networks, which combines Generative Adversarial Network (GAN) and Transformer models. In GAN, the generative model uses the Transformer model to generate false samples, while the discriminative model uses a binary classifier based on convolutional neural networks and attention mechanisms to distinguish between true and false samples. During the training process, reinforcement learning algorithms are added to evaluate and adjust the generated sentences, and the parameters of the generated model are updated. The classification results of the discriminative model are used together with the Bilingual Evaluation Basis Value (BLEU) objective function to evaluate false samples, and the results are fed back to the generating model to guide parameter updates and optimization. Extensive experiments were conducted on a standard English-Chinese machine translation dataset to evaluate our method. Compared with the benchmark model that only uses supervised learning methods, our neural network-based optimization algorithm for English natural translation processing has achieved significant improvements in translation quality. According to statistical comparison, compared with the Transformer model (BLUE = 33.63 and AP = 90%) and the deep learning model based on long-term and short-term memory (BLUE = 30.26 and AP = 83%), the GAN and Transformer models proposed as the best framework exhibit better performance in bilingual evaluation deficiency (BLEU) (34.35) and accuracy (AP = 95%).

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.

Pre-service Teachers' Perspectives on the Integration of Technology in Future Education: Between English Education and Ethics Education Majors

The current study aimed to investigate the perceptions of pre-service teachers regarding the integration of advanced technologies into future education and to examine whether these perceptions differ significantly based on the pre-service teachers' academic majors. The participants consisted of 110 pre-service teachers majoring in Ethics education and English education at a university in Korea's Gyeongsang Province. A questionnaire comprising 59 items was administered, addressing aspects such as interest in the utilization of advanced technologies, resistance to their use, concerns about their application, and expectations for the future of education over the next decade. Statistical analyses, including a paired t-test, an independent <i>t</i>-test, frequency analysis, and analysis of variance, were employed to examine differences based on variables of gender and major. </br>The study revealed statistically significant disparities in the perceptions of pre-service teachers from distinct majors, particularly between English education majors and ethics education majors, concerning the incorporation of advanced technologies in education. English education majors demonstrated notably high awareness, particularly in the domain of neural machine translation. Conversely, ethics education majors demonstrated a lower inclination to embrace the use of advanced technologies in their instructional practices. Additionally, ethics education majors expressed the belief that the role of teachers would become more pivotal in classes integrating these technologies. Considering these research outcomes, it becomes evident that future education for pre-service teachers should underscore comprehensive training in both digital technology education and the development of competencies in areas where artificial intelligence cannot replace the role of educators.

The Impact of Machine Translation on the Development of Tourism Translation From the Perspectives of Translators and Experts in Saudi Arabia

Regardless of its distinction for touring and tourism communication, the subject of machine translation has not been dealt with regarding tourism investigation. This study aims to bridge this gap in knowledge by exploring machine translation (MT) technology in the development of tourism translation from the perspectives of translators and translation experts in Saudi Arabia. To attain the three objectives of the study, the researcher applied the quantitative approach. For data gathering, a 12-item opinion poll was disseminated among a random sample group that consisted of 80 translators and translation experts who were selected at random from several Saudi universities, top translation services companies, and tourism organizations during the current academic year 1445/AH/2024 CE. The findings of this study discovered that many translators and translation experts had positive perspectives on utilizing machine translation (MT) technology in the development of tourism translation around the world today in general and Saudi Arabia in particular. Moreover, the perspectives of these translators and translation experts on the difficulties and intricacy confronted in applying machine translation (MT) technology in the tourism translation industry were highly positive. Over half of the participants (51.42%) perceived that machine translation (MT) can help overcome obstacles encountered by translators in the field of tourism. The findings revealed that translators and translation experts in the realm of the tourism industry have a promising future thanks to the relationship between machine translation (MT) and tourism translation.

Ainu–Japanese Bi-directional Neural Machine Translation: A Step Towards Linguistic Preservation of Ainu, An Under-Resourced Indigenous Language in Japan

This study presents a groundbreaking approach to preserving the Ainu language, recognized as critically endangered by UNESCO, by developing a bi-directional neural machine translation (MT) system between Ainu and Japanese. Utilizing the Marian MT framework, known for its effectiveness with resource-scarce languages, the research aims to overcome the linguistic complexities inherent in Ainu's polysynthetic structure. The paper delineates a comprehensive methodology encompassing data collection from diverse Ainu text sources, meticulous preprocessing, and the deployment of neural MT models, culminating in the achievement of significant SacreBLEU scores that underscore the models' translation accuracy. The findings illustrate the potential of advanced MT technology to facilitate linguistic preservation and educational endeavors, advocating for integrating such technologies in safeguarding endangered languages. This research not only underscores the critical role of MT in bridging language divides but also sets a precedent for employing computational linguistics to preserve cultural and linguistic heritage.

An Evaluation of ChatGPT's Translation Accuracy Using BLEU Score

Traditional views have long held that machine translation cannot achieve the quality and accuracy of human translators, especially in complex language pairs like Persian and English. This study challenges this perspective by demonstrating that ChatGPT-4, with access to vast amounts of multilingual data and leveraging advanced large language model algorithms, significantly outperforms widely utilized open-source machine translation tools and approaches the realm of human translation quality. This research aims to critically assess the translation accuracy of ChatGPT-4 against a traditional open-source machine translation tool from Persian to English, highlighting the advancements in artificial intelligence-driven translation technologies. Using Bilingual Evaluation Understudy scores for a comprehensive evaluation, this study compares the translation outputs from ChatGPT-4 with MateCat, providing a quantitative basis for comparing their accuracy and quality. ChatGPT-4 achieves a BLUE score of 0.88 and an accuracy of 0.68, demonstrating superior performance compared to MateCat, with a 0.82 BLUE score and 0.49 accuracy. The results indicate that the translations generated by ChatGPT-4 surpass those produced by MateCat and nearly mirror the quality of human translations. The evaluation demonstrates the effectiveness of OpenAI's large language model algorithms in improving translation accuracy.

Learning Domain Specific Sub-layer Latent Variable for Multi-Domain Adaptation Neural Machine Translation

Domain adaptation proves to be an effective solution for addressing inadequate translation performance within specific domains. However, the straightforward approach of mixing data from multiple domains to obtain the multi-domain neural machine translation (NMT) model can give rise to the parameter interference between domains problem, resulting in a degradation of overall performance. To address this, we introduce a multi-domain adaptive NMT method aimed at learning domain specific sub-layer latent variable and employ the Gumbel-Softmax reparameterization technique to concurrently train both model parameters and domain specific sub-layer latent variable. This approach facilitates the learning of private domain-specific knowledge while sharing common domain-invariant knowledge, effectively mitigating the parameter interference problem. The experimental results show that our proposed method significantly improved by up to 7.68 and 3.71 BLEU compared with the baseline model in English-German and Chinese-English public multi-domain datasets, respectively.

Dose multimodal machine translation can improve translation performance?

Dose multimodal machine translation can improve translation performance?

Neural machine translation for low resource Indian language: Hindi-Kangri

Neural Machine Translation (NMT) for low resource languages is a challenging task due to unavailability of large parallel corpus. The efficacy of Transformer based NMT models largely depends on scale of the parallel corpus and the configuration of hyperparameters implemented during model training. This study aims to delve into and elucidate the impact of hyperparameters on the performance of NMT models for low resource languages. To accomplish this, a series of experiments are conducted using an open-source Hindi-Kangri corpus to train both supervised and semi-supervised NMT models. Throughout the experimentation process, a significant number of discrepancies were identified within the data-set, necessitating manual correction. The best translation performance evaluated with respect to the metrics such as BLEU (0–1), SacreBLEU (0–100), Chrf (0–100), Chrf+ (0–100), Chrf++ (0–100) and TER (%) is (0.15, 14.98, 41.43, 41.49, 38.77, 68.20) for Hindi to Kangri direction, and (0.283, 28.17, 49.71, 50.64, 48.63, 51.25) for Kangri to Hindi direction.

Automatic Image Caption Generation with Deep Learning

Artificial intelligence research has long focused on the task of automatically defining what is included in a picture or image. This paper presents the CNN and RNN-LSTM models used in the Automatic Caption Generator implementation. It takes into account recent developments in image processing and performing research on translation through automation. The dataset utilized was Flickr8k. We have employed BLEU scores to assess the efficiency of the system that has been described. Based on the scores, one could classify the resulting captions as excellent or awful. The primary uses of this paradigm are virtual assistants, picture indexing, social media, assistive technology for the blind, altering app suggestions, and many more areas. Key Words: Automatic Caption Generator, CNN, RNN-LSTM, Computer vision, Machine translation, Flickr8k dataset, BLEU scores, Performance evaluation, Virtual assistants, Picture indexing, Social media, Assistive technology for the blind, App suggestions.

Harnessing Open Innovation for Translating Global Languages into Indian Lanuages

Understanding foreign languages can be challenging for individuals living in India's diverse linguistic landscapes. We propose a new technology that utilizes machine translation to address this issue, specifically focusing on speech recognition and synthesis. It aims to convert online video resources into Indian languages by integrating open-source technologies like text-to-speech (TTS), speech-to-text (STT) systems, and FFmpeg library to separate or augment audio and video. We used the whisper model, the application that can read up to 60 different Languages in the form of audio as input, and it transcripts the audio into text with segments of sentences based on timestamps. The sentence-based transcription generated by whisper is then translated into the desired language using Google Cloud translate_v2. Later, Each timestamp was individually converted into audio using the Google Cloud text-to-speech service, ensuring the audio fits inside the length of its respective timestamp. The individual audio segments are then augmented to generate the final audio in the desired language. Finally, the audio is attached to the original video, ensuring video-audio synchronization. The accuracy of the translation was verified by comparing the naturalness of the audio with general spoken language standards. This application benefits visually impaired individuals and those who cannot read text, providing them with a means to acquire knowledge in their native languages.

Comparison Between Human Translation and Machine Translation Take Lolita as an Example

Taking the excerpt of Lolita as the research corpus, this paper compares the differences between the manual translation and the machine translation, and finds that the machine translation system has some problems such as inaccuracy in semantic understanding and grammatical errors. In order to improve the quality of machine translation and promote cross-cultural communication and information flow,a classification system of translation errors is established and the errors in machine translation are summarized in detail. The proposed classification system will facilitate a comprehensive understanding of the limitations inherent in machine translation, enable an in-depth analysis of existing challenges, and serve as a valuable reference for enhancing the performance of machine translation systems, thereby elevating the quality and precision of automated translations.

Future Translators’ Linguistic and Non-linguistic Competencies and Skills in The Age of Neural Machine Translation and Artificial Intelligence: A Content Analysis

Artificial Intelligence (AI) and machine learning (ML) are disrupting the way millions of people work. According to the Future of Jobs Report published by the World Economic Forum (WEF 2022), the Fourth Industrial Revolution will result in the disappearance of more than 75 million jobs by 2025. While this is considered bad news to many, the report also predicts the creation of 133 million jobs for the same reason. Among the at-risk jobs are translation and language services jobs which have witnessed a sharp drop in demand due to the adoption of AI and ML technologies in machine translation systems making them capable of producing translations that match the translations produced by expert human translators. Using content analysis method of online translation job advertisements in Saudi Arabia, this study sheds light on the current in-demand translator linguistic and non-linguistic competences and skills from the perspective of employers and matches those with the well-established translator competence models in literature. Through a content analysis of 213 online job advertisements, the study identifies five key competency domains: Language and Culture, Personal and Interpersonal, Service Provision, Technology, and Translation Knowledge. While language proficiency remains crucial, there is a growing emphasis on "soft skills," project management, technological adaptability, and ethical practice. The findings reveal a partial alignment with existing competency models but also highlight potential gaps, particularly regarding strategic planning and document analysis skills. The study underscores the need for translator education to adapt to the evolving demands of the industry, integrating traditional skills with emerging technologies and fostering a culture of lifelong learning. By bridging the gap between academic training and industry expectations, we can better equip future translators to navigate the complexities of the profession and thrive in the age of Neural Machine Translation and AI.