Translation Task Research Articles

The importance of coding and translation between programming languages in sequential activities of pre-service teachers: an approach

AbstractIn the last two decades, computational thinking has gained wide relevance in international educational systems. The inclusion of this new type of thinking poses educational challenges with some underlying research questions that need to be answered to meet these challenges with quality. Thus, this study focuses on analyzing the difficulties that teachers in initial training experience have, when carrying out translation tasks of programming languages used by certain educational robots, in this case, the Cubetto. For this purpose, a specific learning sequence has been designed to work with different programming languages (Cubetto, Bee-Bot, Scratch) and natural language. The work of early childhood and elementary trainee teachers in these tasks has been analyzed using a descriptive approach. The main results are: (1) some of the difficulties encountered are clearly caused by the Cubetto hardware (regardless of the language to which it is translated) and (2) the designed learning sequence has enabled coding skills to be improved remarkably. We conclude that translation tasks between programming languages are necessary in initial teacher training to improve their ability programming and their computational thinking, and for them to be able to detect the disadvantages and benefits of educational robots in their transposition to the classroom.

Challenges of Translating Idiomatic Expressions: A Cross-Linguistic Analysis at a University in Hanoi, Vietnam

This study investigates the challenges students face when translating idiomatic expressions between English and Vietnamese and explores how linguistic and cultural factors influence the translation process. Utilizing a combination of translation task analysis and focus group discussions, the research reveals that students frequently rely on literal translations, resulting in inaccurate or nonsensical outcomes due to their inability to grasp the figurative meanings of idioms. Cultural mismatches, particularly the absence of equivalent expressions in Vietnamese, further complicate translation efforts, leading to significant misunderstandings. Additionally, linguistic barriers, such as syntactic differences between English and Vietnamese, hinder students' ability to produce natural and accurate translations of idiomatic expressions. The findings emphasize the critical role of cultural knowledge and syntactic flexibility in effective idiomatic translation. To address these challenges, the study recommends that translation training programs place greater emphasis on idiomatic understanding and cross-cultural competence. This study contributes to the growing body of literature on idiomatic translation by highlighting the interplay of linguistic and cultural factors and suggesting strategies to enhance translation accuracy.

Traduire le théâtre de Gil Vicente, spécificités de traduction et possibles apports liés à l’intelligence artificielle

Translating the theater of Gil Vicente poses a significant challenge, that only specialists about the author, with some rare exceptions, have translated his plays. Between the nuances of the text, the archaisms of the language, and the specificities of theatrical writing, this playwright has seen few of his plays, also known as autos, translated into French. At a time when the relationship between the translator and technological advancements is becoming increasingly intertwined, it is important to analyze how certain software, particularly those of Artificial Intelligence, perform in the task of translation and what their impact might be on future translations of Gil Vicente.

Study on an Intelligent English Translation Method Using an Improved Convolutional Neural Network Model

This study uses an enhanced convolutional neural network (CNN) model for English translation. Traditional translation methods often struggle with complex language structures, prompting the adoption of deep learning techniques, particularly CNNs, in natural language processing. The research outlines CNN fundamentals and their relevance in language processing, elucidating the design and implementation of an improved CNN model. To address CNN limitations in maintaining coherence during the translation of lengthy texts, historical attention mechanisms are incorporated to enhance translation performance. Experimental validation conducted in MATLAB demonstrates notable improvements in translation task performance, evidenced by significant increases in BLEU scores. Results highlight the model's capacity to integrate contextual information, thereby enhancing translation coherence and accuracy. Additionally, the study establishes a mathematical framework for augmenting CNNs with attention mechanisms, providing valuable insights for the development of intelligent English translation systems.

Cross-Task Differences in Frontocentral Cortical Activations for Dynamic Balance in Neurotypical Adults.

Although significant progress has been made in understanding the cortical correlates underlying balance control, these studies focused on a single task, limiting the ability to generalize the findings. Different balance tasks may elicit cortical activations in the same regions but show different levels of activation because of distinct underlying mechanisms. In this study, twenty young, neurotypical adults were instructed to maintain standing balance while the standing support surface was either translated or rotated. The differences in cortical activations in the frontocentral region between these two widely used tasks were examined using electroencephalography (EEG). Additionally, the study investigated whether transcranial magnetic stimulation could modulate these cortical activations during the platform translation task. Higher delta and lower alpha relative power were found over the frontocentral region during the platform translation task when compared to the platform rotation task, suggesting greater engagement of attentional and sensory integration resources for the former. Continuous theta burst stimulation over the supplementary motor area significantly reduced delta activity in the frontocentral region but did not alter alpha activity during the platform translation task. The results provide a direct comparison of neural activations between two commonly used balance tasks and are expected to lay a strong foundation for designing neurointerventions for balance improvements with effects generalizable across multiple balance scenarios.

A Study on Benjamin’s Theory of Translation

Walter Benjamin is one of the most creative and important critics of the early 20th century, and his work “The Task of Translation” stands as one of the most influential translation theories. It has greatly influenced the traditional notions of faithfulness and the “original language first” principle, laying the theoretical foundation for deconstructionist translation theory.

Efficient Adaptation: Enhancing Multilingual Models for Low-Resource Language Translation

This study focuses on the neural machine translation task for the TR-EN language pair, which is considered a low-resource language pair. We investigated fine-tuning strategies for pre-trained language models. Specifically, we explored the effectiveness of parameter-efficient adapter methods for fine-tuning multilingual pre-trained language models. Various combinations of LoRA and bottleneck adapters were experimented with. The combination of LoRA and bottleneck adapters demonstrated superior performance compared to other methods. This combination required only 5% of the pre-trained language model to be fine-tuned. The proposed method enhances parameter efficiency and reduces computational costs. Compared to the full fine-tuning of the multilingual pre-trained language model, it showed only a 3% difference in the BLEU score. Thus, nearly the same performance was achieved at a significantly lower cost. Additionally, models using only bottleneck adapters performed worse despite having a higher parameter count. Although adding LoRA to pre-trained language models alone did not yield sufficient performance, the proposed method improved machine translation. The results obtained are promising, particularly for low-resource language pairs. The proposed method requires less memory and computational load while maintaining translation quality.

Unpaired data training enables super-resolution confocal microscopy from low-resolution acquisitions.

Supervised deep-learning models have enabled super-resolution imaging in several microscopic imaging modalities, increasing the spatial lateral bandwidth of the original input images beyond the diffraction limit. Despite their success, their practical application poses several challenges in terms of the amount of training data and its quality, requiring the experimental acquisition of large, paired databases to generate an accurate generalized model whose performance remains invariant to unseen data. Cycle-consistent generative adversarial networks (cycleGANs) are unsupervised models for image-to-image translation tasks that are trained on unpaired datasets. This paper introduces a cycleGAN framework specifically designed to increase the lateral resolution limit in confocal microscopy by training a cycleGAN model using low- and high-resolution unpaired confocal images of human glioblastoma cells. Training and testing performances of the cycleGAN model have been assessed by measuring specific metrics such as background standard deviation, peak-to-noise ratio, and a customized frequency content measure. Our cycleGAN model has been evaluated in terms of image fidelity and resolution improvement using a paired dataset, showing superior performance than other reported methods. This work highlights the efficacy and promise of cycleGAN models in tackling super-resolution microscopic imaging without paired training, paving the path for turning home-built low-resolution microscopic systems into low-cost super-resolution instruments by means of unsupervised deep learning.

Translating Traditional Folk Poetry: The Perception of Translators and Translation Students and the Reality in Saudi Society

Despite the massive presence and influence of traditional folk poetry (TFP) within Saudi society, the efforts to translate such works remain very limited. While many authors and researchers have examined the impact of this literary genre and illustrated its prominence, academic investigations into the difficulties of translating TFP works are almost nonexistent. This is also true about studies that focus on the overarching sentiment regarding these efforts among those who are associated with the field of language and translation and among members of the general public. This paper thus aims at examining these perspectives and outlining the challenges translation trainees face when performing tasks in this field. The data for this study was collected through the dissemination of a questionnaire and the analysis of a translation task assigned to university students in an academic program for English language and translation. The qualitative and quantitative analysis of the data shows that the level of acceptance of TFP translation efforts is strongly connected to the degree of association with the field of languages and translation, with individuals from outside these circles being less supportive of initiatives in this domain. Additionally, the paper identified several obstacles and challenges encountered by translation students working on TFP texts. The paper concludes with a number of recommendations and suggestions that can, hopefully, contribute to the efforts to raise awareness about translating TFP and to the process of improving the current state of translation within this field.

Mainshock–aftershock sequence simulation via latent space encoding of generative adversarial networks

AbstractAftershocks (ASs) following strong mainshocks (MSs) can exacerbate structural damage or lead to collapse. However, the scarcity of recorded data necessitates reliance on artificial sequences, which have difficulty in characterizing the time‐frequency correlation between MSs and ASs. This study innovatively converts the AS time history prediction into an image translation task, exploiting the invertible transformation between accelerograms and time‐frequency representations. An encoder–decoder neural network is developed to encode the MS information into the latent space of a pre‐trained generative adversarial network, enabling accurate AS predictions through the decoder. The integration of seismic parameters further improves the AS prediction performance. Comparative analyses demonstrate that the proposed method outperforms the traditional ones on accuracy and robustness and reproduces the non‐stationarity of ASs.

THE UNIQUE TRANSLATION STRATEGY OF NOVEL TITLES

Titles of literary works such as novels plays an important role in attracting possible readers. Novel titles should also reflect the contents of the books. When a book is translated to other languages, the title should be translated accordingly. Novel titles are not always translated literally in the target language, they may vary from one country to another. For example, in “Harry Potter and the philosopher stone” in the UK, the phrase ‘philosopher stone’ is translated into ‘sorcerer’s stone’ in the US and ‘batu bertuah’ (miracle stone) in Indonesia. In French it is even translated into ‘Harry Potter a l’ecole des sorciers’ (Harry Potter in the school of witches). This study aims to investigate the translation strategies of English novel titles to Indonesian. By searching the internet and from private book collection, the writer collect 50 English novels which have been translated and published in Indonesian. Based on the writer’s personal preference, all the novels belong to thriller/suspense genre. The data collected were 50 English titles and their translations in Indonesian. Before arriving at this number of data, a preliminary selection has been done by eliminating the target text titles that were using literal translation strategy. This is deliberately done to discover the unique translation strategy of novel titles and to find out the reason for the specific translation strategy. Three main theories are used to analyse the novel titles, they are functions of title translation as suggested by Nord (2005) and Viezzi model translation (2013) and Vinay & Dalbernet (1995) translation strategies. In translating the titles, equivalence is pursued, but if it is not possible, there are other ways to accomplish it. The results show strategies such as adaptation, modulation, equivalence, and transposition are used to translate novel titles to Indonesian. Meanwhile based on Viezzi model, the target titles are categorised into explicit, indicative of the genre, suggestive, and seductive. This study would raise the translators’ awareness to choose more communicative and creative titles for literary translation tasks, including novels and other literary works.

Investigation of the Use of Computer-aided Translation (CAT) Tools in Freelancing Practice in Saudi Arabia

To cope with large information content in today’s multilingual Saudi Arabia, translation services are essential. Like any other industry, translation practice was heavily affected by technological advances. Professionals in the translation field use technology like Computer-Aided Translation (CAT) tools to complete their translation tasks. Freelance translators are not exempt from this. This study examines the utilization of CAT tools in the freelance industry in Saudi Arabia. Fulford and Granell-Zafra (2005) have proposed a model that incorporates both information and Communication Technology (ICTs)and CAT tools, facilitating a diverse array of activities such as document creation, information search and retrieval, communication, business management, marketing, procurement, and translation. While CAT tools support document creation, business management, and translation, ICT tools are responsible for the remaining activates . This research aims to thoroughly analyze the characteristics of Saudi freelance translators who use CAT tools, evaluate their usage of CAT tools, and acquire valuable insights into their overall attitudes towards CAT tool. The data was gathered using the survey method and distributed among 98 Saudi freelance translators. The data was analyzed using SPSS descriptive analysis and frequency tables showing percentages of each respondent. The results show that Saudi freelance translators employed general-purpose software like MS Word and MS PowerPoint, whereas less interest was given to special-purpose software which represent CAT tools . The vast majority of the participants agreed on the positive effect of CAT tools. Finally,it may be beneficial to conduct further research to gain a more comprehensive understanding of the topic.

Residual Pyramidal GAN (RP-GAN) for crack detection and prediction of crack growth in engineered cementitious composites

Crack detection has recently gained credence in the field of engineering automation. Engineered cementitious composites (ECC) are a durable, and environmentally friendly construction material and a suitable replacement for conventional concrete. As such, there is a need to automate the process of detecting the unique ductile crack formation and growth in ECC. This research focuses on detecting ECC cracks, accurately mapping the crack morphology, and predicting the crack growth patterns of this advanced material. We design and present a novel generative adversarial algorithm named Residual Pyramidal Generative Adversarial Network (RP-GAN), capable of ECC crack detection. RP-GAN is an enhanced and improved version of the typical Pix2Pix GAN for paired image-to-image translation. It is an extensive network that could be used for macro and micro-segmentation purposes in addition to domain translation tasks. In this research, we first investigate the ability of RP-GAN to detect ECC micro-cracks and map the dimensions of each crack. Secondly, we compared RP-GAN’s crack detection results with those of known state-of-the-art architectures. We then developed an algorithmic pipeline for live crack tracking using an experimental framework. Finally, we evaluate the possibility of detecting ECC crack growth and propagation using sequential images by re-engineering RP-GAN and fusing Convolutional-LSTM into its architecture. The mean intersection over union (IOU) test results of RP-GAN for ECC crack detection equaled 81.28%, and 77.76% for ECC crack propagation prediction.

The cross-script cognate effect in spoken and written second language production: A study based on Chinese-English bilinguals.

Previous studies using cognates with the same writing system have found cognate facilitation effect in the lexical processes of spoken and typewritten productions and cognate interference effect in the sub-lexical process of typewritten production. This study focused on cross-script cognates, Chinese-English, which have different writing systems, and explored cognate effects based on the input and output modalities by using a Chinese-English translation task. Experiment 1 was under visual input modality and investigated the cross-script cognate effect in all three output modalities: spoken, typewritten and handwritten. Results revealed a cognate facilitation effect in the lexical processes across all three output modalities. However, it showed a cognate facilitation effect rather than a cognate interference effect in the sub-lexical process of typewritten production. Experiment 2 was under auditory input modality and focused on exploring cross-script cognate effect on typewritten and handwritten modalities, finding a consistent result on cognate effects with Experiment 1. Both experiments showed higher accuracy for cognates, and there was no significant difference in cgnate effect between visual and auditory inputs. In summary, these findings indicated that the use of cross-script cognates could effectively mitigate cognate interference. While spoken, handwritten and typewritten production share lexical processes, differences emerge in sub-lexical processes, with spoken production being less influenced by orthography. Furthermore, combining the results of Experiments 1 and 2, typewritten production may lean towards the phonological route while handwritten production may favour the direct lexical-orthographic route in the sub-lexical processes.

Balancing quality and efficiency: An improved non-autoregressive model for pseudocode-to-code conversion

Pseudocode can efficiently represent algorithm logic, but manual conversion to executable code requires more time. Recent works have applied autoregressive (AR) models to automate pseudocode-to-code conversion, achieving good results but slow generation speed. Non-autoregressive (NAR) models offer the advantage of parallel generation. However, they face challenges in effectively capturing contextual information, leading to a potential degradation in the quality of the generated output. This paper presents an improved NAR model for balancing quality and efficiency in pseudocode conversion. Firstly, two strategies are proposed to address out-of-vocabulary and repetition problems. Secondly, an improved NAR model is built using linear smoothing and adaptive techniques in the transition matrix, which can mitigate the “winner takes all” effect. Finally, a new synthesis potential metric is proposed for evaluating pseudocode conversion. Experimental results show that the proposed method matches AR model performance while accelerating generation over 10-fold. Further, the proposed NAR model reduces the gap with the AR model in terms of the BLEU score on the EN-DE and DE-EN tasks of the WMT14 machine translation.

The Role of Cognitive Schema Theory in the Translation from Arabic into English

This study investigates the role of schema theory in translation and aims to gain knowledge that may progress AI-related domains through the simulation of cognition and AI. Schema theory hypothesizes that translators use mental frameworks (schemata) to organize and interpret information. In the context of translation, schemata play a crucial role in knowledge representation, affecting limitations made during the translation process. A cognitive approach is employed and qualitative methods are used in the analysis. A corpus is collected from the translation of a SL Arabic novel (Frankenstein in Baghdad by A. Saadawi) into TL English, covering domains and language pairs. The translation process is analyzed to identify cognitive patterns employed by the translator as they apply schemata to achieve cross-cultural renditions. Conclusions reveal that schema activation significantly impacts translation choices, influencing how the translator interprets and conveys meaning across languages. Cognitive patterns shed light on how cultural and linguistic factors influence the transfer of information between SL and TL. Additionally, the study uncovers variations in schema utilization across different translation tasks and the adaptability and flexibility of cognitive processes in response to varying contexts and linguistic challenges. By deepening our understanding of schema theory, this research contributes to the design, training, and assessment of AI algorithms. It also provides valuable insights into the cognitive mechanisms underlying successful cross-linguistic communication and offers practical implications for translators engaged in intercultural exchange. Finally, this paper recommends that AI systems can drive advancements in MT and other AI-related fields of prior knowledge to produce accuracy and more closely with native speakers by using schema theory concepts.

Dynamic Contrast Enhanced MRI Mapping of Vascular Permeability for Evaluation of Breast Cancer Neoadjuvant Chemotherapy Response Using Image-to-Image Conditional Generative Adversarial Networks.

Dynamic contrast enhanced (DCE) MRI is a non-invasive imaging technique that has become a quantitative standard for assessing tumor microvascular permeability. Through the application of a pharmacokinetic (PK) model to a series of T1-weighed MR images acquired after an injection of a contrast agent, several vascular permeability parameters can be quantitatively estimated. These parameters, including Ktrans, a measure of capillary permeability, have been widely implemented for assessing tumor vascular function as well as tumor therapeutic response. However, conventional PK modeling for translation of DCE MRI to PK vascular permeability parameter maps is complex and time-consuming for dynamic scans with thousands of pixels per image. In recent years, image-to-image conditional generative adversarial network (cGAN) is emerging as a robust approach in computer vision for complex cross-domain translation tasks. Through a sophisticated adversarial training process between two neural networks, image-to-image cGANs learn to effectively translate images from one domain to another, producing images that are indistinguishable from those in the target domain. In the present study, we have developed a novel image-to-image cGAN approach for mapping DCE MRI data to PK vascular permeability parameter maps. The DCE-to-PK cGAN not only generates high-quality parameter maps that closely resemble the ground truth, but also significantly reduces computation time over 1000-fold. The utility of the cGAN approach to map vascular permeability is validated using open-source breast cancer patient DCE MRI data provided by The Cancer Imaging Archive (TCIA). This data collection includes images and pathological analyses of breast cancer patients acquired before and after the first cycle of neoadjuvant chemotherapy (NACT). Importantly, in good agreement with previous studies leveraging this dataset, the percentage change of vascular permeability Ktrans derived from the DCE-to-PK cGAN enables early prediction of responders to NACT.

Neural architecture search for image super-resolution: A review on the emerging state-of-the-art

Nowadays, complex and expensive neural architectures are seen by many as a way to improve the performance of existing models in image recognition, voice recognition, translation, and other tasks. Such a perspective caused an increased interest in expert architecture engineering within Deep Learning. Fueled by this interest, neural architecture search originated as a promising way to automate the tedious process of constructing a deep neural network by hand. Over the last five years, we have seen an increasing number of works focusing all efforts on studying the impact of automating deep neural network design. The spotlight has recently turned from automatically discovering classification models to other more complex tasks. Motivated by a desire for high-resolution images in real-world user-centered and expert computer vision applications, architecture search for super-resolution image restoration centers in approaches capable of automatically finding efficient and well-performing models. Here, we present a survey that, beyond delving into an overview of modern approaches to automatic neural network design, focuses on the recollection and study of neural architecture search approaches that have directed their efforts at the super-resolution image restoration tasks and future lines of research found within this emerging area of study.

Enhancing Chinese–Braille translation: A two-part approach with token prediction and segmentation labeling

Visually assistive systems for the visually impaired play a pivotal role in enhancing the quality of life for the visually impaired. Assistive technologies for the visually impaired have undergone a remarkable transformation with the advent of deep learning and sophisticated assistive devices. In particular, the paper utilizes the latest machine translation models and techniques to accomplish the Chinese–Braille translation task, providing convenience for visually impaired individuals. The Traditional end-to-end Chinese–Braille translation approach incorporates Braille dots and Braille word segmentation symbols as tokens within the model’s vocabulary. However, our findings reveal that Braille word segmentation is significantly more complex than Braille dot prediction. The paper proposes a novel Two-Part Loss (TPL) method that treats these tasks distinctly, leading to significant accuracy improvements. To enhance translation performance further, we introduce a BERT-Enhanced Segmentation Transformer (BEST) method. BEST leverages knowledge distillation techniques to transfer knowledge from a pre-trained BERT model to the translate model, mitigating its limitations in word segmentation. Additionally, soft label distillation is employed to improve overall efficacy further. The TPL approach achieves an average BLEU score improvement of 1.16 and 5.42 for Transformer and GPT models on four datasets, respectively. In addition, The work presents a two-stage deep learning-based translation approach that outperforms traditional multi-step and end-to-end methods. The proposed two-stage translation method achieves an average BLEU score improvement of 0.85 across four datasets.

Practical Guidelines for Implementing Translation into the EFL Classroom

The use of languages other than the target one in foreign language teaching (FL) contexts has been interestingly gaining ground recently. A multitude of research has shown that though dogmatically no other language than the target language should be used in the FL classroom, L1 is used by many teachers and learners. It is noteworthy that managing the class and building relationships with the learners are crucial to the success of the lesson; yet they cannot be realized via the target language, especially with less proficient language learners. This article falls into the realm of translation use in foreign language teaching. It attempts primarily to bridge the gap between theory and practice, through suggesting a set of practical guidelines for teachers wishing to implement translation into their classroom. These guidelines are meant to model the judicious use of L1, and thus answer teachers’ queries about where to introduce L1 exactly within the lesson procedure, and by means of which tasks. In order to achieve a maximum of teacher-friendliness, the suggested guidelines are designed in reference to a lesson procedure based on the textual approach, with the implementation of translation-based tasks throughout the phases of the lesson, while mentioning the aim behind the use of L1 each time a translation task is introduced