Rule-based Machine Translation Research Articles

Implications of using AI in Translation Studies: Trends, Challenges, and Future Direction

This review paper provides an overview of the use of artificial intelligence (AI) in translation studies. The paper discusses the various AI techniques that have been used in translation, including statistical machine translation, rule-based machine translation, neural machine translation, and hybrid machine translation. The paper also explores the advantages and limitations of each model, as well as their applications in translation studies. Additionally, the paper reviews the various techniques for evaluating the effectiveness of AI models in translation and their advantages and limitations. The challenges and limitations of AI in translation, such as the handling of idioms, metaphors, and cultural nuances, are also discussed, along with research directions for improving AI-based translation. The review also discusses the representation of AI in literature and the arts, delves into the academic opportunities, and investigates its impact on human lives. Furthermore, AI's ethical and social implications in translation, such as job displacement, data privacy, and bias and fairness, are examined. Finally, the paper summarizes the main findings, implications, and recommendations for future research directions in AI-based translation studies. Overall, this review paper provides some insights into the current state of AI in translation and its potential for improving the field.

Exploration on Advanced Intelligent Algorithms of Artificial Intelligence for Verb Recognition in Machine Translation

This article aimed to address the problems of word order confusion, context dependency, and ambiguity in traditional machine translation (MT) methods for verb recognition. By applying advanced intelligent algorithms of artificial intelligence, verb recognition can be better processed and the quality and accuracy of MT can be improved. Based on Neural machine translation (NMT), basic attention mechanisms, historical attention information, dynamically obtain information related to the generated words, and constraint mechanisms were introduced to embed semantic information, represent polysemy, and annotate semantic roles of verbs. This article used the Workshop on machine translation (WMT), British National Corpus (BNC), Gutenberg, Reuters Corpus, OpenSubtitles corpus, and enhanced the data in the corpus. The improved NMT model was compared with traditional NMT models, Rule Based machine translation (RBMT), and Statistical machine translation (SMT). The experimental results showed that the average verb semantic matching degree of the improved NMT model in 5 corpora was 0.85, and the average Bilingual Evaluation Understudy (BLEU) score in 5 corpora was 0.90. The improved NMT model in this article can effectively improve the accuracy of verb recognition in MT, providing new methods for verb recognition in MT.

The metamorphosis of machine translation: The rise of neural machine translation and its challenges

Machine translation refers to the process of using computers to translate source language into target language, which has undergone significant transformations since its inception, with the current mainstream neural machine translation achieving satisfactory translation performance. This paper overviews the three developmental stages of machine translation: rule-based machine translation, statistical machine translation, and neural machine translation, with a focus on neural machine translation. It introduces the key models that emerged in the development process of neural machine translation, namely the recurrent neural network encoder-decoder model, recurrent neural network search model, and Transformer, and compares their strengths and limitations. Other relevant technologies and models developed alongside neural machine translation are also discussed. Addressing the current challenges of neural machine translation, the paper delves into issues of overfitting, low-resource translation, structural optimization of Transformer models, and enhancement of neural machine translation interpretability. Finally, the paper explores the prospects of applying neural machine translation to multimodal translation.

Rule-Based Machine Translation for the Automatic Translation of Vietnamese Sign Language

Rule-Based Machine Translation for the Automatic Translation of Vietnamese Sign Language

On the Concept of Translation Unit in a Machine Translation Framework

The article looks at the question whether the concept of translation unit might apply to the sphere of machine translation and whether the size of the unit influences the quality of translation. While modern machine translation systems offer an acceptable level of quality, a number of problems mainly related to the structural organization of the text remain unresolved, hence the question posed in the paper. The article offers a review of modern readings of the concept and pays special attention to the question whether the scope of the term changes depending on whether the object of research is the target text or the translation process. The paper also provides a quick look on the research methods for both text-oriented and process-oriented approaches, such as comparative analysis of language pairs and Think Aloud Protocol. Based on a review of existing machine translation models, each of them is analyzed to answer the question whether a unit of translation can be defined for a given system and what its size is. It is concluded that a unit of translation can be viewed as either a unit of analysis or a unit of processing with respect to text-oriented and process-oriented perspectives on to the study of translation. The unit of translation has a dynamic character and influences the quality of the target text. In machine translation, the unit of translation as a unit of analysis is not applicable for systems based on probabilistic non-linguistic methods. For rule-based machine translation systems, both readings of the unit of translation concept are applicable, but hardly go beyond a single sentence. Accordingly, at least one type of translation problem – intra-textual relations resolutions – remains largely unaddressed in the present state of affairs in machine translation.

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.

Sentiment induced phrase-based machine translation: Robustness analysis of PBSMT with senti-module

Every type of machine translation system (i.e. neural, statistical, rule-based machine translation system) is equal important to build a sophistical hybrid machine translation system. Keeping this fact in my mind, I concentrate to improve statistical machine translation system with more natural way. In this paper, I try to preserve sentiment after translation to improve the overall accuracy of the machine translation system. So, I introduced senti-model here. A senti-model (sentiment model), translation model, language model, and distortion model are incorporated on the top of the beam search algorithm for decoding. At first, sentiment information is learned and modeled with translation probability by using this algorithm. Thereafter, I decode the source sentences-based on the contextual information. Overall procedure of translation modeling with a sentiment, parameter estimation for it, and senti-translation decoding (decoding with the sentiment model) are presented with empirical evidence. Experiments on a benchmark English–Hindi dataset shows that the proposed model is capable to improve the accuracy (in terms of 4.66 BLEU points, 4.09 LeBleu points, 4.67 NIST points, 5.71 RIBES points) significantly and preserves sentiment 7.79% more than the state-of-the-art technique.

COMPARATIVE STUDY OF MACHINE TRANSLATION TECHNIQUES

Machine translation (MT) is an approach to give knowledge and train computer to translate the sentence from one language to another language with the same fluent and meaning. MT is evaluated by the techniques that are giving the best translating accuracy. The four major techniques are Statistical Machine Translation, Rule-based Machine Translation, Example-based Machine Translation and Neural Machine Translation. We will see comparison and evaluation between these techniques in this particular paper with the examples and BLEU scores.

Translation of English Language into Urdu Language Using LSTM Model

English to Urdu machine translation is still in its beginning and lacks simple translation methods to provide motivating and adequate English to Urdu translation. In order to make knowledge available to the masses, there should be mechanisms and tools in place to make things understandable by translating from source language to target language in an automated fashion. Machine translation has achieved this goal with encouraging results. When decoding the source text into the target language, the translator checks all the characteristics of the text. To achieve machine translation, rule-based, computational, hybrid and neural machine translation approaches have been proposed to automate the work. In this research work, a neural machine translation approach is employed to translate English text into Urdu. Long Short Term Short Model (LSTM) Encoder Decoder is used to translate English to Urdu. The various steps required to perform translation tasks include preprocessing, tokenization, grammar and sentence structure analysis, word embeddings, training data preparation, encoder-decoder models, and output text generation. The results show that the model used in the research work shows better performance in translation. The results were evaluated using bilingual research metrics and showed that the test and training data yielded the highest score sequences with an effective length of ten (10).

Analysis on the Recent Trends in Machine Translation

Machine translation is to translate one language into another language, which has undergone a great evolution. The model of machine translation has been continuously improved, aiming to make the translation effect closer to the artificial translation. This article briefly summarizes the development history of machine translation, and introduces the main models of each stage of development. The initial machine translation mode is the Rule Based Machine Translation (RBMT) and Statistical Machine Translation (SMT). Recent mainstream translation approach enables Neural Machine Translation (NMT). It includes the input and the output, attention mechanism, and BLEU evaluation method. On this basis, there are also many expansion and innovation models, such as GPKD and other models to improve the evaluation effect. In general, machine translation can replace a part of human translation. However, it cannot completely replace human beings, because of the different human thinking and machine logic. People and machines have to cooperate with each other to improve the common efficiency.

Progress in Machine Translation

After more than 70 years of evolution, great achievements have been made in machine translation. Especially in recent years, translation quality has been greatly improved with the emergence of neural machine translation (NMT). In this article, we first review the history of machine translation from rule-based machine translation to example-based machine translation and statistical machine translation. We then introduce NMT in more detail, including the basic framework and the current dominant framework, Transformer, as well as multilingual translation models to deal with the data sparseness problem. In addition, we introduce cutting-edge simultaneous translation methods that achieve a balance between translation quality and latency. We then describe various products and applications of machine translation. At the end of this article, we briefly discuss challenges and future research directions in this field.

Developing a Rule-Based Machine-Translation System, Ewondo–French–Ewondo

Machine translation (MT) significantly contributes to democratizing access to textual information across multiple languages and is established as a dynamic language service in the global multilingual society. Not surprisingly, the attractiveness of the MT market has stirred up spectacular innovations, driven by artificial intelligence in the digital technology industry. The commercial stakes of the industry has led to massive investments in the development of automatic translation systems for languages of wider communication and an increased marginalization of minority languages in this avenue. This article reports on the on-going development of a low-tech, rule-based MT system for Ewondo, a Bantu low-resourced language spoken in Cameroon. The project aims to fill the gap in access to MT services in the target minority language community and to generate parallel corpora from and into the Ewondo language.

Identifikasi Ekspresi Idiomatik Menggunakan Distributional Semantic Based Approach dan Truth Discovery

Idiomatic expressions are phrases that consist of a sequence of two or more words that have a meaning that cannot be predicted from the meaning of the individual words that compose it. Idiomatic expressions exist in almost all languages ??but are difficult to extract because there is no algorithm that can precisely decipher the structure of idiomatic expressions, so most rule-based machine translation systems generally translate idiomatic expressions by translating word for word their constituents, but the translation results do not produce the true meaning of the idiomatic expression. Based on this problem, the author tries to do research on the identification of the use of idiomatic expressions in Indonesian sentences. First, the author conducts the sentence classification process using BERT to find out whether the sentence contains idiomatic expressions or not. Furthermore, idiomatic expressions are identified based on distributional semantic based approach and then validated automatically using the Truth Discovery method. From the research conducted, the identification of idiomatic expressions in Indonesian sentences using Distributional Semantic Based Approach and Truth Discovery obtained an accuracy of 0.82; precision 1.0; recall 0.64 and f1-score 0.78.

Automatic Extraction of English-Chinese Translation Templates Based on Deep Learning

Translation templates are an important cause of knowledge in machine translation (MT) systems. Their quality and scale directly influence the performance of MT systems. How to obtain high-quality and efficient translation templates from corpora has become a hot topic in recent study. In this paper, a tree to String alignment template (TAT) based on syntactic structure is proposed. This template describes the alignment between the source language syntax tree and the target language string. The syntactic structure, a large number of construction tags, and variables are introduced into the template, which enables the syntactic model to deal with discontinuous phrases and has the ability of generalization. Templates can be used in syntactic statistics, case-based, and rule-based MT systems according to different decoders. ATTEBSC algorithm is a basic method to learn translation templates by comparing sentence pairs. It demands that sentence pairs be constructed in a precise comparison structure ahead of time, but there are no strict guidelines on how to do it. In this paper, we propose a method to calculate the specific comparison scheme using the longest common subsequence (LCS) and use the normalized LCS distance to screen sentences with high similarity and then use the ATTEBSC algorithm to automatically remove the template. Experiments show that this method is easy and effective, and many expensive templates can be learned.

Artificial Intelligence-based Recognition of Chinese Characters and Translation of Classical Chinese: Focusing on the Current State of Korea

This paper introduces the current status of artificial intelligence-based recognition of Chinese characters and machine translation of Chinese characters (Classical Chinese texts), which is currently underway in Korea, and suggests future tasks. For the recognition of Chinese characters, this paper discusses the Chinese character segmentation using the Mask R-CNN algorithm and the Chinese character classification method using the Inception- Resnet-V2 algorithm, and explores the significance and limitations of the construction of Chinese character forms database. Regarding machine translation, this paper looks at the development of machine translation technology from Rule-Based Machine Translation (RBMT) to Neural Machine Translation (NMT); then, introduces the principles of neural network machine translation and techniques applied to improve performance. There are two types of machine translation for Classical Chinese texts: the machine translator provided by Baidu and the translator developed by the Institute for the Translation of Korean Classics (ITKC). Although the Baidu machine translator has various errors, the overall translation performance is somewhat higher than that of ITKC's machine translator. The ITKC's translator performs better than the Baidu translator in certain areas, but falls short of the Baidu translator in translating various types of Classical Chinese texts. In order to improve the performance of the machine translation of Classical Chinese texts, it is necessary tobuild a cloud-based translation platform, expand a high-quality parallel corpus, develop an additional system to improve machine translation performance, and develop a highly reliable translation evaluation method.

Bilingual Dictionaries: From Theory to Computerization

This paper suggests a computationally-enhanced model of an English –Arabic dictionary based on a systematically empirical linguistic analysis of the source language and target language systems in contradistinction to the introspective intuitions of bilingual lexicographers. In this model, computerized text corpora and bilingual semantic concordances play a key role in turning out a reliable bilingual dictionary that does not only serve the purposes of all types of Bilingual Dictionary users but will also be a robust bilingual repertoire in bilingual Natural Language Processing systems such as rule-based Machine Translation.

Scaling up a Hybrid MT System: From low to full resources

This article describes a hybrid approach to machine translation (MT) that is inspired by the rule-based, statistical, example-based, and other hybrid machine translation approaches currently used or described in academic literature. It describes how the approach was implemented for language pairs using only limited monolingual resources and hardly any parallel resources (the METIS-II system), and how it is currently implemented with rich resources on both the source and target side as well as rich parallel data (the PaCo-MT system). We aim to illustrate that a similar paradigm can be used, irrespectively of the resources available, but of course with an impact on translation quality.

Recent advances in Apertium, a free/open-source rule-based machine translation platform for low-resource languages

This paper presents an overview of Apertium, a free and open-source rule-based machine translation platform. Translation in Apertium happens through a pipeline of modular tools, and the platform continues to be improved as more language pairs are added. Several advances have been implemented since the last publication, including some new optional modules: a module that allows rules to process recursive structures at the structural transfer stage, a module that deals with contiguous and discontiguous multi-word expressions, and a module that resolves anaphora to aid translation. Also highlighted is the hybridisation of Apertium through statistical modules that augment the pipeline, and statistical methods that augment existing modules. This includes morphological disambiguation, weighted structural transfer, and lexical selection modules that learn from limited data. The paper also discusses how a platform like Apertium can be a critical part of access to language technology for so-called low-resource languages, which might be ignored or deemed unapproachable by popular corpus-based translation technologies. Finally, the paper presents some of the released and unreleased language pairs, concluding with a brief look at some supplementary Apertium tools that prove valuable to users as well as language developers. All Apertium-related code, including language data, is free/open-source and available at https://github.com/apertium.

Inflection rules for Marathi to English in rule based machine translation

Machine translation is important application in natural language processing. Machine translation means translation from source language to target language to save the meaning of the sentence. A large amount of research is going on in the area of machine translation. However, research with machine translation remains highly localized to the particular source and target languages as they differ syntactically and morphologically. Appropriate inflections result correct translation. This paper elaborates the rules for inflecting the parts-of-speech and implements the inflection for Marathi to English translation. The inflection of nouns, pronouns, verbs, adjectives are carried out on the basis of semantics of the sentence. The results are discussed with examples.

Application of Translation Technologies in the Translation of IMTFE Transcripts

Machine translation has grown very rapidly in recent times due to the developments in big data, artificial intelligence, and cloud computing software and techniques. The first generation of Rule-Based Machine Translation has been replaced by fourth generation of Neural Machine Translation based on complex deep learning and networking models. Translation models have also undergone tremendous changes. The traditional translation model fails to meet the needs of the modern language service industry. There still exists doubt whether Translation technologies could enhance the quality in the translation of non-technical texts. So far no one has discussed the application of the technologies in the translation of IMTFE Transcripts. This paper aims to prove that translation technologies can be applied to the translation of IMTFE Transcripts which the author works on. By analyzing the text features of IMTFE Transcripts, and applying different translation technologies in the translation, the author finds that the quality and efficiency of translation have been greatly enhanced. It is concluded that translation technologies can be used to facilitate translation of texts of different kinds. In spite of their drawbacks, translators can still benefit a lot from the adoption of translation technologies.