English Machine Translation Research Articles

A Transformer-Based Yoruba to English Machine Translation (TYEMT) System with Rouge Score

Automated translation systems for some indigenous Nigerian languages like the Yoruba, have historically been limited by the lack of large, high- quality bilingual text and effective approaches to modeling. This paper presents introduces an approach to bi-directional Yoruba-English text-to-text machine translation utilizing deep learning technique, specifically Transformer models. Transformer models, which utilizes self-attention mechanisms to improve translation quality and efficiency. The system was trained and evaluated on a newly curated Yoruba- English parallel corpus, which significantly augments existing resources. Experimental results demonstrate that the Transformer-based model performs translation accurately and fluently, achieving a ROUGE (Recall-Oriented Understudy for Gisting Evaluation) score improvement of 0.4649. This work not only advances the frontiers of Yoruba-English machine translation but also enriches a wider domain in the field of multilingual Natural Language processing (NLP) by addressing challenges associated with translating between languages with limited resources. Future studies include enhancing the available parallel corpus and exploring hybrid models that combine the strengths of both RNN and Transformer architectures.

Language-attached Transfer Restrictions in Neural Machine Translation: An Analysis of Tao Te Ching

This study focuses on the language-attached transfer restrictions in Chinese to English neural machine translation (NMT) of Tao Te Ching, an ancient Chinese philosophical text, which aims to identify and analyze the specific challenges and limitations encountered by NMT systems when translating linguistic features that are unique to the source language and how these challenges impact the accuracy and quality of the translated text in English. It contributes to the understanding of the complexities involved in machine translation of ancient Chinese texts and proposes strategies to mitigate the identified transfer restrictions, ultimately aiming to bridge the gap between machine translation capabilities and the richness of Chinese philosophical literature.

TRACING THE FOOTPRINT OF WORLD ENGLISHES ON MACHINE TRANSLATION TOOLS

With the rise in the population who uses English as a Foreign or Second language, World Englishes has become more focus of English language teaching field specialists. As a consequence, English varieties which are present in supplementary tools used in the process of language teaching and learning took on a new meaning. As one of the commonly used supplementary tools is machine translation tools, the current study aims to explore the machine translation outputs tendency considering grammar-based, spelling-based, and lexical variations between varieties of Englishes regarding Google Translate outputs. Source texts were extracted from Turkish National Corpus. A total of seventy-three Turkish to English machine translation outputs were examined regarding two dominant varieties, American English and British English, with the aim of figuring out the machine translation tendency of translation outputs. The analysis of the research showed that Google Translate provides users with mostly American English compared to British English.

Design of English Lexical Analysis System for Machine Translation Based on Multimodal Neural Network

Information resources have become a very important source of wealth, and due to differences in language and writing, technological information exchange between countries has become difficult. In this article, we propose a data augmentation method based on statistical models. A framework for a multimodal output model is proposed, taking into account the semantic relevance and importance between different languages. This framework is based on text sequence to sequence framework, decoupled, and a network architecture based on dual stream attention mechanism is designed. A multimodal interactive neural network layer was added between the encoder and decoder, achieving the fusion of multilingual domain information. It compensates for the influence of text on traditional translation generation systems in the process of English Chinese translation, and solves the problems of inaccurate translation results and low similarity with the original text in English Chinese machine translation. The system proposed in this article mainly consists of preprocessing module, lexical analysis and segmentation module, part of speech tagging and phrase analysis module, translation rule construction module, decoding module, translation generation module, etc. The experimental results show that using an improved generation system, compared with traditional generation systems, improves the accuracy of translation generation, has certain advantages, and is more practical.

Spoken‐to‐written text conversion for enhancement of Korean–English readability and machine translation

AbstractThe Korean language has written (formal) and spoken (phonetic) forms that differ in their application, which can lead to confusion, especially when dealing with numbers and embedded Western words and phrases. This fact makes it difficult to automate Korean speech recognition models due to the need for a complete transcription training dataset. Because such datasets are frequently constructed using broadcast audio and their accompanying transcriptions, they do not follow a discrete rule‐based matching pattern. Furthermore, these mismatches are exacerbated over time due to changing tacit policies. To mitigate this problem, we introduce a data‐driven Korean spoken‐to‐written transcription conversion technique that enhances the automatic conversion of numbers and Western phrases to improve automatic translation model performance.

Are Emotions Conveyed Across Machine Translations? Establishing an Analytical Process for the Effectiveness of Multilingual Sentiment Analysis with Italian Text


 
 
 Abstract Natural language processing (NLP) is being widely used globally for a variety of value-creation tasks ranging from chat-bots and machine translations to sentiment and topic analysis and multilingual large language models (LLMs). However, most of the advances are initially implemented within the English language framework, and it takes time and resources to develop comparable resources in other languages. The advances in machine translations have enabled the rapid and effective conversion of content in global languages into English and vice-versa. This creates potential opportunities to apply English language NLP methods and tools to other languages via machine translations. However, although this idea is powerful, it needs to be validated and processes and best practices need to be developed and kept updated. The present research is an effort to contribute to the development of best practices and an evaluation framework. We present a systematic and repeatable state-of-the-art process to evaluate the viability of applying English language sentiment analysis tools to Italian text by using multiple English language machine translation mechanisms such that it can be easily extended to other languages.
 
 

Research on Feature Extraction and Optimization Measures of University English Translation for Machine Learning

Abstract In the background of increasing translation content, it is no longer possible to rely solely on human translation to solve the problem of cross-language communication, and thus machine translation technology has gradually become an important means to solve the language barrier. In this paper, the semantic content features are extracted from university English using a semantic model based on fuzzy semantic mapping relations. Optimize the model feature extraction based on concept set context matching, introduce Super-Concept and Sub-Concept, calculate the concept semantic translation similarity, and add them into the translation decoding to get the translation optimization results. In this way, the university’s English machine translation system is constructed and evaluated. The maximum values of data recall and context matching rate of the system in this paper are 71.1% and 99.6%, respectively, and the BLEU values of this paper’s model are higher than those of the CNN model and the Transformer machine translation model in the tests of different slicing granularity. The system has high data recall, context matching rate, and translation accuracy, which is feasible and practical in university English translation, and lays a research foundation for further optimization of university English machine translation.

English and Sanskrit Language Comparison and Machine Translation Approaches.

English and Sanskrit Language Comparison and Machine Translation Approaches.

English translation evaluation method based on BP neural network

In order to solve the problems of machine translation efficiency and translation quality, this paper proposes an English translation evaluation system based on BP neural network algorithm. This method provides users with a more intelligent machine translation service experience. With the help of BP neural network algorithm, taking English online translation as the research object, Google's translation quality is the best, with an error frequency of only 167, while Baidu translation and iFLYTEK translation in China have a high error rate of 266 and 301 respectively, which is much higher than Google translation. A model of machine translation evaluation based on neural network algorithm is proposed to better solve the disadvantages of traditional English machine translation. The results show that the machine translation system based on neural network algorithm can further optimize the problems existing in machine translation, such as insufficient use of information and large scale of model parameters, and further improve the performance of neural network machine translation.

Research on Optimization Strategies for Accuracy of English Automatic Language Translation under Machine Learning Framework

Abstract The mainstream machine translation model Transformer is completely based on the self-attention mechanism for translation operation, but there are still some problems, such as not being able to combine the syntactic structure information of the natural language for translation, which leads to problems such as mistranslation and omission. In this paper, for the problem that the position encoding obtained by traditional RNN and attention mechanism machine translation models using a fixed formula does not contain contextual information, the source language sequences containing contextual positional information are obtained by introducing a bidirectional long-short-term memory network and a tree-shaped long-short-term memory network, trained horizontally and vertically, respectively, and the self-attention mechanism is used in Tree-LSTM for the prediction of the contribution of the decision that The relative position information between words is preserved to the maximum extent, and finally, the Bi-Tree-LSTM translation model based on positional encoding optimization is constructed. The performance of the model is tested on four datasets: general, legal, business, film, and television, and the BLEU value of the model translation is analyzed under low data resources and increased sentence length, and then a 4000-sentence long English text is translated to check the wrong sentences and analyze the translation quality. It was found that the BLEU values of this paper’s model are 33.5, 35.2, 31.7, and 34.4 in the four types of text tests, which are the highest among the models. The BLEU of this paper’s model at 5K data volume has been as high as 26.14 points, which is 2.72 points higher than the highest score of the rest of the machine translation models at 50K data volume. The BLEU scores for 8-18 word sentences consistently remain above 45, and the peak performance is superior to that of other models. 4000 sentences of English long text translation, the total number of error sentences is 54, accounting for 1.39% of the whole text, which is lower than that of the Transformer model’s 7.15%, and the performance is in line with the expectation of the optimization design. This paper provides a new idea and useful exploration for optimizing English machine translation accuracy.

A quality assessment of Korean–English patent machine translation

Abstract This paper aims to investigate the quality Korean–English patent translations by three machine translation (MT) engines based on automatic and human evaluations of Korean to English Patent Automatic Translation (K2E-PAT), a pattern-based statistical MT; and Patent Translate and WIPO Translate, both neural MTs. For title translations, WIPO Translate scored the highest in automatic and human evaluations, while results were mixed for the other two MTs. K2E-PAT slightly outperformed Patent Translate in automatic evaluation, whereas Patent Translate outperformed K2E-PAT in human evaluation. For abstract translations, Patent Translate scored the highest in automatic evaluation, followed by WIPO Translate and K2E-PAT. In human evaluation, the ranking order was the same as that of title translations, with WIPO Translate scoring the highest on average. The results indicated correlations between automatic and human evaluations, and the NMTs subject to the current study still do not render satisfactory gist translation from Korean to English.

Research on the Application of Computer Big Data Technology in English Online Translation

Aiming at the disadvantages of the traditional rule-based machine translation model that the English translation results are not accurate enough and it is difficult to accurately describe the relationship between words, the English machine translation model based on the semantic network is designed and improved. The algorithm analyses the English grammatical rules, then performs Gaussian marginalization on the semantics to obtain the rectangular window function, obtains the window feature vector, projects the semantic information entropy data, and adds the semantic correlation factors to the information entropy and information gain of the text., So as to obtain the semantic nonlinear spectral characteristics. Experimental results show that the designed English machine translation model has high translation accuracy and stability.

Development of a XLM-Encoded Machine-Readable Dictionary for Yoruba Word Sense Disambiguation

The development of the disambiguation component of a Yorùbá to English machine translation system is hindered by several factors. One of these is the lack of machine readable sense inventory for ambiguous Yorùbá words. This study addressed the problem by developing a machine readable dictionary for ambiguous Yoruba verbs. To achieve this, ambiguous Yorba verbs and their translations were collected from existing bilingual dictionaries. The collected lexicons were transformed into machine readable format using the Extensible Markup Language (XML) Format. The accuracy of translation of the machine readable dictionary was evaluated using mean opinion score, with a score of 4.37 over the scale of 5. This study covered the total number of ninety-three (93) monosyllabic verbs with two hundred and forty-one (241) senses, which gives a coverage of 69.5% of the ambiguous monosyllabic verbs in Yoruba Language. The sense inventory was also used as a component of a Yoruba Word Sense Disambiguation system, and an accuracy of 94.6% was achieved. This study concludes that the digitized data can increase the accuracy of Word Sense Disambiguation component of a Yorùbá to English machine translation system.

Arabic and English Relative Clauses and Machine Translation Challenges

Arabic and English Relative Clauses and Machine Translation Challenges

Retracted: LSTM-Based Attentional Embedding for English Machine Translation

Retracted: LSTM-Based Attentional Embedding for English Machine Translation

Cross-lingual extreme summarization of scholarly documents

The number of scientific publications nowadays is rapidly increasing, causing information overload for researchers and making it hard for scholars to keep up to date with current trends and lines of work. Recent work has tried to address this problem by developing methods for automated summarization in the scholarly domain, but concentrated so far only on monolingual settings, primarily English. In this paper, we consequently explore how state-of-the-art neural abstract summarization models based on a multilingual encoder–decoder architecture can be used to enable cross-lingual extreme summaries of scholarly texts. To this end, we compile a new abstractive cross-lingual summarization dataset for the scholarly domain in four different languages, which enables us to train and evaluate models that process English papers and generate summaries in German, Italian, Chinese and Japanese. We present our new X-SCITLDR dataset for multilingual summarization and thoroughly benchmark different models based on a state-of-the-art multilingual pre-trained model, including a two-stage pipeline approach that independently summarizes and translates, as well as a direct cross-lingual model. We additionally explore the benefits of intermediate-stage training using English monolingual summarization and machine translation as intermediate tasks and analyze performance in zero- and few-shot scenarios. Finally, we investigate how to make our approach more efficient on the basis of knowledge distillation methods, which make it possible to shrink the size of our models, so as to reduce the computational complexity of the summarization inference.

Evaluation of Commercially Available Machine Interpretation Applications for Simple Clinical Communication.

Accessing professional medical interpreters for brief, low risk exchanges can be challenging. Machine translation (MT) for verbal communication has the potential to be a useful clinical tool, but few evaluations exist. We evaluated the quality of three MT applications for English-Spanish and English-Mandarin two-way interpretation of low complexity brief clinical communication compared with human interpretation. Audio-taped phrases were interpreted via human and 3 MT applications. Bilingual assessors evaluated the quality of MT interpretation on four assessment categories (accuracy, fluency, meaning, and clinical risk) using 5-point Likert scales. We used a non-inferiority design with 15% inferiority margin to evaluate the quality of three MT applications with professional medical interpreters serving as gold standards. Proportion of interpretation exchanges deemed acceptable, defined as a composite score of 16 or greater out of 20 based on the four assessment categories. For English to Spanish, the proportion of MT-interpreted phrases scored as acceptable ranged from 0.68 to 0.84, while for English to Mandarin, the range was from 0.62 to 0.76. Both Spanish/Mandarin to English MT interpretation had low acceptable scores (range 0.36 to 0.41). No MT interpretation met the non-inferiority threshold. While MT interpretation was better for English to Spanish or Mandarin than the reverse, the overall quality of MT interpretation was poor for two-way clinical communication. Clinicians should advocate for easier access to professional interpretation in all clinical spaces and defer use of MT until these applications improve.

English Machine translation System Based on Neural Network Algorithm

In order to solve the problems of machine translation efficiency and translation quality, this paper proposes a research method of machine English translation evaluation system based on BP neural network algorithm. This method provides users with a more intelligent machine translation service experience. With the help of BP neural network algorithm, taking English online translation as the research object, Google's translation quality is the best, with an error frequency of only 167, while Baidu translation and iFLYTEK translation in China have a high error rate of 266 and 301 respectively, which is much higher than Google translation. A model of machine translation evaluation system based on neural network algorithm is proposed to better solve the disadvantages of traditional English machine translation. The results show that the machine translation system based on neural network algorithm can further optimize the problems existing in machine translation, such as insufficient use of information and large scale of model parameters, and further improve the performance of neural network machine translation.

Retracted: A Chaotic Neural Network Model for English Machine Translation Based on Big Data Analysis.

[This retracts the article DOI: 10.1155/2021/3274326.].

Application of LSTM Neural Network Technology Embedded in English Intelligent Translation.

With the rapid development of computer technology, the loss of long-distance information in the transmission process is a prominent problem faced by English machine translation. The self-attention mechanism is combined with convolutional neural network (CNN) and long-term and short-term memory network (LSTM). An English intelligent translation model based on LSTM-SA is proposed, and the performance of this model is compared with other deep neural network models. The study adds SA to the LSTM neural network model and constructs the English translation model of LSTM-SA attention embedding. Compared with other deep learning algorithms such as 3RNN and GRU, the LSTM-SA neural network algorithm has faster convergence speed and lower loss value, and the loss value is finally stable at about 8.6. Under the three values of adaptability, the accuracy of LSTM-SA neural network structure is higher than that of LSTM, and when the adaptability is 1, the accuracy of LSTM-SA neural network improved the fastest, with an accuracy of nearly 20%. Compared with other deep learning algorithms, the LSTM-SA neural network algorithm has a better translation level map under the three hidden layers. The proposed LSTM-SA model can better carry out English intelligent translation, enhance the representation of source language context information, and improve the performance and quality of English machine translation model.