Machine Translation Applications Research Articles

Deep Learning Applications in Natural Language Processing and Optimization Strategies

In recent years, deep learning has made significant advancements in the field of natural language processing (NLP), driving the development of language understanding and generation tasks. Deep learning utilizes multi-layer neural networks to automatically extract and represent complex data features. This paper first introduces the basic principles of deep learning and mainstream frameworks (such as TensorFlow and PyTorch) and discusses core NLP tasks, including word embedding, language modeling, and text generation. Subsequently, it analyzes specific practices of deep learning applications in text classification, machine translation, automatic summarization, dialogue systems, and speech recognition. Further discussion covers model optimization methods, including structural optimization (such as RNN, LSTM, GRU, and Transformer), data preprocessing and feature engineering, hyperparameter tuning, and accelerated computation. Finally, it explores cutting-edge optimization strategies like federated learning, model compression, self-supervised learning, and transfer learning, proposing future research directions and challenges. This paper aims to systematically analyze the applications and optimization strategies of deep learning in NLP.

On the Application of Machine Translation in Legal Terminology Translation

On the Application of Machine Translation in Legal Terminology Translation

Enhancement of English-Bengali Machine Translation Leveraging Back-Translation

An English-Bengali machine translation (MT) application can convert an English text into a corresponding Bengali translation. To build a better model for this task, we can optimize English-Bengali MT. MT for languages with rich resources, like English-German, started decades ago. However, MT for languages lacking many parallel corpora remains challenging. In our study, we employed back-translation to improve the translation accuracy. With back-translation, we can have a pseudo-parallel corpus, and the generated (pseudo) corpus can be added to the original dataset to obtain an augmented dataset. However, the new data can be regarded as noisy data because they are generated by models that may not be trained very well or not evaluated well, like human translators. Since the original output of a translation model is a probability distribution of candidate words, to make the model more robust, different decoding methods are used, such as beam search, top-k random sampling and random sampling with temperature T, and others. Notably, top-k random sampling and random sampling with temperature T are more commonly used and more optimal decoding methods than the beam search. To this end, our study compares LSTM (Long-Short Term Memory, as a baseline) and Transformer. Our results show that Transformer (BLEU: 27.80 in validation, 1.33 in test) outperforms LSTM (3.62 in validation, 0.00 in test) by a large margin in the English-Bengali translation task. (Evaluating LSTM and Transformer without any augmented data is our baseline study.) We also incorporate two decoding methods, top-k random sampling and random sampling with temperature T, for back-translation that help improve the translation accuracy of the model. The results show that data generated by back-translation without top-k or temperature sampling (“no strategy”) help improve the accuracy (BLEU 38.22, +10.42 on validation, 2.07, +0.74 on test). Specifically, back-translation with top-k sampling is less effective (k=10, BLEU 29.43, +1.83 on validation, 1.36, +0.03 on test), while sampling with a proper value of T, T=0.5 makes the model achieve a higher score (T=0.5, BLEU 35.02, +7.22 on validation, 2.35, +1.02 on test). This implies that in English-Bengali MT, we can augment the training set through back-translation using random sampling with a proper temperature T.

Indigenous language technology in the age of machine learning

ABSTRACT Most modern language technology for proofing tools, machine translation and other applications is based on machine learning. However, very few Indigenous languages have the necessary amount of texts for making tools based on this technology. When most language technology is based on large language models (LLMs), it bears the risk of most of Indigenous language online text being produced by neural text generation. The result would be that online texts cannot be trusted as a source for authentic Indigenous languages anymore. An alternative is the work done at UiT – The Arctic University of Norway during the last 20 years, based on linguistics. Sámi language tools have been made available for both industry and language communities, with open licenses. These have been widely used by translators, teachers and various software companies. The article analyzes the following four parts of language technology development: language data, language tool development, making the tools available to users, and ethical use of available language technology tools. We make extensive use of the CARE principles, and discuss the shortcomings of existing software and data licensing schemes. Finally, we introduce a 3D table to help classify language technology projects with respect to their suitability for Indigenous languages.

Word Closure-Based Metamorphic Testing for Machine Translation

With the wide application of machine translation, the testing of Machine Translation Systems (MTSs) has attracted much attention. Recent works apply Metamorphic Testing (MT) to address the oracle problem in MTS testing. Existing MT methods for MTS generally follow the workflow of input transformation and output relation comparison, which generates a follow-up input sentence by mutating the source input and compares the source and follow-up output translations to detect translation errors, respectively. These methods use various input transformations to generate the test case pairs and have successfully triggered numerous translation errors. However, they have limitations in performing fine-grained and rigorous output relation comparison and thus may report many false alarms and miss many true errors. In this paper, we propose a word closure-based output comparison method to address the limitations of the existing MTS MT methods. We first propose word closure as a new comparison unit, where each closure includes a group of correlated input and output words in the test case pair. Word closures suggest the linkages between the appropriate fragment in the source output translation and its counterpart in the follow-up output for comparison. Next, we compare the semantics on the level of word closure to identify the translation errors. In this way, we perform a fine-grained and rigorous semantic comparison for the outputs and thus realize more effective violation identification. We evaluate our method with the test cases generated by five existing input transformations and the translation outputs from three popular MTSs. Results show that our method significantly outperforms the existing works in violation identification by improving the precision and recall and achieving an average increase of 29.9% in F1 score. It also helps to increase the F1 score of translation error localization by 35.9%.

Stameering Speech Signal Segmentation and Classification using Machine Learning

Stuttering or Stammering is a speech defect within which sounds, syllables, or words are rehashed or delayed, disrupting the traditional flow of speech. Stuttering can make it hard to speak with other individuals, which regularly have an effect on an individual's quality of life. Automatic Speech Recognition (ASR) system is a technology that converts audio speech signal into corresponding text. Presently ASR systems play a major role in controlling or providing inputs to the various applications. Such an ASR system and Machine Translation Application suffers a lot due to stuttering (speech dysfluency). Dysfluencies will affect the phrase consciousness accuracy of an ASR, with the aid of increasing word addition, substitution and dismissal rates. In this work we focused on detecting and removing the prolongation, silent pauses and repetition to generate proper text sequence for the given stuttered speech signal. The stuttered speech recognition consists of two stages namely classification using ANN and testing in ASR. The major phases of classification system are Re-sampling, Segmentation, Pre Emphasis, Epoch Extraction and Classification. The current work is carried out in UCLASS Stuttering dataset using MATLAB with 4% to 6% increase in accuracy by ANN.

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.

Implementation of Neural Machine Translation in Translating from Indonesian to Sasak Language

Language translation is part of Natural Language Processing, also known as Machine Translation, which helps the process of learning foreign and regional languages using translation technology in sentence form. In Lombok, there are still people who are not very fluent in Indonesian because Indonesian is generally only used at formal events. This research aimed to develop a translation model from Indonesian to Sasak. The method used was the Neural Machine Translation with the Recurrent Neural Network - Long Short Term Memory architecture and the Word2Vec Embedding with a sentence translation system. The dataset used was a parallel corpus from the Tatoeba Project and other open sources, divided into 80% training and 20% validation data. The result of this research was the application of Neural Machine Translation with the Recurrent Neural Network - Long Short Term Memory algorithm, which could produce a model with an accuracy of 99.6% in training data and 71.9% in test data. The highest ROUGE evaluation metric result obtained on the model was 88%. This research contributed to providing a translation model from Indonesian to Sasak for the local community to facilitate communication and preserve regional language culture.

Data Augmentation with Translation Memories for Desktop Machine Translation Fine-tuning in 3 Language Pairs

This study aims to investigate the effect of data augmentation through translation memories for desktop machine translation (MT) fine-tuning in OPUS-CAT. It also focuses on assessing the usefulness of desktop MT for professional translators. Engines in three language pairs (English → Turkish, English → Spanish, and English → Catalan) are fine-tuned with corpora of two different sizes. The translation quality of each engine is measured through automatic evaluation metrics (BLEU, chrF2, TER and COMET) and human evaluation metrics (ranking, adequacy and fluency). Overall evaluation results indicate promising quality improvements in all three language pairs and imply that the use of desktop MT applications such as OPUS-CAT and fine-tuning MT engines with custom data in a translator’s desktop can potentially provide high-quality translations aside from their advantages such as privacy, confidentiality and low use of computation power.

Design and Implementation of Foreign Language Recognition and Translation APP Based on Artificial Intelligence

Globalization has created the demand for cross-language communication, which has promoted the development of machine translation applications (APPs) based on artificial intelligence (AI) for foreign language recognition and translation. The purpose of this study is to develop and implement a mobile application program that can accurately identify and translate foreign languages. APP adopts advanced artificial intelligence technology, uses optical character recognition (OCR) technology to identify the text in images, and uses neural network architecture based on transformer for machine translation. In the data preprocessing stage, text cleaning, word segmentation and other preprocessing steps are performed to create accurate translation input. A translation model is trained on a large bilingual mapping data set to learn the mapping knowledge relationship between the source language and the target language. After a series of tests and experiments, the application of foreign language identification and translation shows the high accuracy and speed of multilingual input and output. The accuracy of this application is 0.9, and the maximum delay is only 180 milliseconds. Although there is still room for improvement in the background of professional fields or complex scenes, this application is an effective tool for cross-language communication. Future work should focus on further optimizing translation models, improving the accuracy of translation for specific domain terms, and enhancing the personalized service capabilities of the APP.

The application of machine translation in automatic dubbing in China: A case study of the feature film Mulan

The use of artificial intelligence (AI) for audiovisual dubbing has become increasingly popular due to its ability to improve content production and dissemination. In particular, the application of machine translation (MT) to audiovisual content has resulted in more efficient and productive AI content generation. To assess the quality of MT-dubbed videos, this study proposed the use of the new FAS model. This model adapts the FAR model put forward by Pedersen (2017), with the “R (Readability)” parameter replaced with “S” to include synchrony: this amendment responds to research on dubbing quality that identifies the need to explore better methods for synchronization between audio and video. Using Mulan (Caro, 2020) – an English-language Disney feature film released in 2020 – as a case study, this article evaluates the quality of automatically dubbed videos generated by YSJ (Ren Ren Yi Shi Jie), an MT platform for audiovisual products in China. By analysing errors in functional equivalence, acceptability, and synchrony, the study assesses whether China’s latest MT engine can meet the demand for quality dubbing and improve cross-cultural communication. The findings show that whereas China’s present MT platform can generate a moderately acceptable result, there are still semantic errors, idiomaticity problems, and synchrony errors which may lead to incorrect translations and consequently possible misunderstanding by viewers. Overall, this study sheds light on the current state of AI-dubbing technology in China and highlights areas for improvement.

Applying machine translation to Chinese–English subtitling: Constraints and challenges

Focusing on the topic of applying machine translation (MT) to Chinese–English subtitling, the subject of the present study, this article first analyses the current literature and introduces the general constraints in Chinese–English subtitling from three perspectives: technical, cultural, and textual. Technical constraints are imposed by the limited time and space available for each subtitle; cultural constraints relate to the disparities in the beliefs, values, customs, behaviours, and artefacts of different cultural groups; and textual constraints predominantly manifest in the differences between the source and target languages and the segmented nature of subtitles. In order to respond to these constraints and to achieve conciseness, comprehensibility, and coherence in the translated subtitles, this study highlighted condensation, context, and coordination as the key strategies to adopt. However, these strategies pose considerable challenges for MT in Chinese–English subtitling. First, the common practice of full translation by most MT tools tends to work against making subtitles concise. Second, the lack of relevant contextual knowledge limits the ability of MT to generate appropriate translations. Third, the segmented display of subtitles makes it challenging for MT to capture and reflect the logic in the source text, on the one hand, and to produce coherent output in self-contained segments, on the other. To illustrate these constraints and challenges, this article provides examples of bilingual subtitles from American Factory (Bognar & Reichert, 2019), an Oscar Winner for Best Documentary Feature, and compares the official subtitles with those generated by three popular MT tools in Chinese–English translation. The study investigated the efficacy of MT subtitling and its potential to produce quality subtitles; and this article proposes possible solutions and suggestions for improving the quality of MT in subtitling.

Application of Neural Machine Translation with Attention Mechanism for Translation of Indonesian to Seram Language (Geser)

The Seram language (Geser) is one of the regional languages in Kabupaten Seram Bagian Timur of Maluku Province which has been classified by the Language Office as an endangered language. This study uses the Neural Machine Translation (NMT) method in an effort to preserve the Seram (Geser) language. The NMT method has proven to be effective compared to SMT in overcoming the challenges of language translation by using the attention mechanism to improve translation accuracy. The data used in this study were obtained through interviews of 3538 parallel corpus, 255 Indonesian vocabularies and 269 Seram (Geser) vocabularies. The result showed that using 708 test data without Out-of Vocabulary (OOV) the BLUE Score was 0.90518992895191 or 90.518%.

The use and abuse of artificial intelligence-enabled machine translation in the EFL classroom: An exploratory study

The integration of machine translation (MT) and artificial intelligence (AI) has significantly enhanced the precision of MT systems producing translations that rival the quality of skilled human translators. This innovation has broadened the applicability of AI-enabled MT systems attracting users for a variety of purposes. This paper explores the following areas: the application of AI-enabled MT, specifically Google Translate (GT) in English as a Foreign Language (EFL) classroom. Empirical findings and recent research indicate that students are increasingly reliant on MT particularly in higher educational settings where English is not their first language. The purpose of this research is to provide insight on the practices, beliefs and goals of EFL learners who use GT in the classroom. Data was gathered from 234 university students through a questionnaire along with pre- and post-tests to compare the quality of writing drafts created with and without the aid of GT. The findings are consistent with those of previous research conducted worldwide highlighting the immense potential of AI-enabled MT in not only boosting students' learning experiences but also cultivating more independent learners. The study also indicates that students have positive attitudes towards GT and employ diverse search strategies to address various language-related challenges.

Development of English-to-Igala machine translation system using neural machine translation with attention mechanism

Translation is a matter of absolute necessity in the globally united and yet; linguistically and culturally separated world in which we live. Translation allows individuals who speak different languages to communicate and understand one another. The obvious limitations in human translation in terms of speed, scalability and high cost gave birth to the quest for alternative means of translation which is machine translation. Machine translation involves the use of computers to translate from one natural language (source) to another natural language (target). Machine translation applications can be developed using rule-based, example- based and statistical-based technologies. Machine translation applications have been developed to translate English language text to Igala language text using rule-based and example-based approaches. Recently, Neural Machine Translation (NMT) emerged as a new paradigm that swiftly superseded other methods of machine translation evolved with the development of deep learning. This study is aimed at developing a system that can translate contents rendered in English language to Igala language using the Neural Machine Translation with attention mechanism approach. The Neural Machine Translation model for English-to-Igala translation was built with encoder-decoder architecture with an attention layer. The model was trained with a dataset using English-Igala parallel corpus that contains 50,000 parallel sentences. The dataset was partitioned into training, validation and test set in the ratio of 80/20 percent. The model was implemented using python programming language. After training, validation and testing, the output of the model was tested on a corpus of 276 selected English texts using the Bilingual Evaluation Understudy (BLEU) method for evaluating Machine Translation system. An accuracy of 71.0% was obtained. The adoption of this model will play crucial role in facilitating everyday interaction, information exchange and active participation in society among Igala people. It will give Igala people access to information in Igala language that will enhance their standard of living as it will enable them to participate in abundant business opportunities available in the online community, and ensure proper integration in the emerging information society.

THE USE OF PARALLEL CORPORA IN TEACHING LANGUAGES AND TRANSLATION PRACTICE

Nowadays the methodology of teaching foreign languages and translation practice involves using and development of modern computer applications called parallel corpora of texts of various genres. Such developments haven‘t been developed in Kazakhstan yet, though parallel texts or so-called bitexts were used for comparative analysis in applied linguistics long before.In teaching translation practice, parallel corpora can be applied for getting referential information, for getting samples of translation made by professionals. They can be used for studying the techniques and methods of translation. In teaching a foreign language, parallel corpora can be applied to choose the most appropriate equivalents of the studied vocabulary, trace its meaning in authentic contexts.The paper considers a number of methodological techniques when using corpora, methods for their construction, types of corpora, as well as the use of special programs for parallel texts. Currently, machine translation itself is based on parallel texts. In the future, a parallel corpus with fully aligned and marked translation units will bring translation studies in the Kazakh language to a new level. Such a corpus can act as a translation memory for the creation of professional machine translation applications, including a simultaneous interpreter program for a conference, an application for translating the spoken text into written text in another language.

Twi Machine Translation

French is a strategically and economically important language in the regions where the African language Twi is spoken. However, only a very small proportion of Twi speakers in Ghana speak French. The development of a Twi–French parallel corpus and corresponding machine translation applications would provide various advantages, including stimulating trade and job creation, supporting the Ghanaian diaspora in French-speaking nations, assisting French-speaking tourists and immigrants seeking medical care in Ghana, and facilitating numerous downstream natural language processing tasks. Since there are hardly any machine translation systems or parallel corpora between Twi and French that cover a modern and versatile vocabulary, our goal was to extend a modern Twi–English corpus with French and develop machine translation systems between Twi and French: Consequently, in this paper, we present our Twi–French corpus of 10,708 parallel sentences. Furthermore, we describe our machine translation experiments with this corpus. We investigated direct machine translation and cascading systems that use English as a pivot language. Our best Twi–French system is a direct state-of-the-art transformer-based machine translation system that achieves a BLEU score of 0.76. Our best French–Twi system, which is a cascading system that uses English as a pivot language, results in a BLEU score of 0.81. Both systems are fine tuned with our corpus, and our French–Twi system even slightly outperforms Google Translate on our test set by 7% relative.

Challenges of machine translation application to teaching ESP to construction students

The article deals with problems of translation teaching, namely machine translation for ESP students. The study aims to conduct a comparative analysis of machine and human translation of construction terminology, identify causes of errors, provide recommendations for improving quality of students’ translation via post-editing as well as developing their interdisciplinary skills using CAT tools. The main research methods include comparative and contrastive analysis as well as the quantitative method. The research material is presented by tittles of construction students’ qualification papers translated from Ukrainian into English. Quality of machine translation is affected by peculiarities of construction terminology due to harmonization of terms according to Eurocodes. The authors prove that application of software to translating construction texts without further proofreading or post-editing by students themselves entails errors including distortion of terminology and, consequently, meaning. There are presented reasons for lexical errors caused by peculiarities of translating multicomponent terms, discrepancies in translating prepositions. The article is intended for a wide range of specialists interested in translating construction texts and teaching ESP. Based on the results obtained, the authors develop recommendations for translating construction texts by using machine translation accompanied by students’ post-editing.

Quality Evaluation of C-E Translation of Legal Texts by Mainstream Machine Translation Systems—An Example of DeepL and Metasota

Despite significant progress made in machine translation technology and the ongoing efforts in practical and commercial application of neural machine translation systems, their performance in vertical fields remains unsatisfactory. To avoid misunderstandings and excessive expectations of a specific machine translation system, this research selected legal texts as its real data research object. The text translation tasks were accomplished using two popular neural machine translation systems, DeepL and Metasota, both domestically and internationally, and evaluated using internationally recognized BLEU algorithm to reflect their Chinese-to-English translation performance in legal fields. Based on the determined BLEU score, the study adopted an artificial analysis method to analyze the grammatical aspects of the machine translation output, including the accuracy of terminology usage, word order, subject-verb agreement, sentence structure, tense, and voice to enable readers to have a rational understanding of the gap between machine translation and human translation in legal text translation, and objectively assess the application and future development prospects of machine translation in legal text fields. The experimental results indicate that machine translation systems still face challenges in achieving high-quality legal text translations and meeting practical needs, and that further post-translation editing research is needed to improve the accuracy of legal text translation.

An Analysis of the Evaluation of the Translation Quality of Neural Machine Translation Application Systems

ABSTRACT Neural machine translation (NMT) is applied to generate a more reliable and accurate translation practice as the most cutting-edge technology. In recent years, NMT has achieved gratifying results. However, the main obstacle for market-oriented NMT application systems appears to suffer from weak translation quality that fails to meet users’ needs. This paper focuses on the machine translation of political documents and implements six dominant NMT application systems in the market to evaluate their translation quality. The evaluation process further employs both BLEU and NIST technical evaluation algorithms and re-verifies the results with the manual evaluation method called the “Score Ranking System” to compare the performances of the six NMTs in Chinese-English translations of political documents. Through diagnosis and evaluation of the problems and errors in NMTs, the paper eventually proposes the “Cue Lexicon+” model to remedy prominent problems. Besides, the “NMT+ Lexicon Intelligent Translation Assistant” soft is developed and the “Cue Lexicon+” is integrated into the NMT application systems to further improve the translation quality, providing a reference and research basis to increase the performance and update the NMT application systems.