Statistical Translation Research Articles

BPE and morphologically segmented phrase based statistical machine translation system for Indian languages to resource constrained language Bodo

BPE and morphologically segmented phrase based statistical machine translation system for Indian languages to resource constrained language Bodo

Indonesian to Bengkulu Malay Statistical Machine Translation System

Machine translation is an automatic tool that can process language translation from one language to another. This research focuses on developing Statistical Machine Translation (SMT) from Indonesian to Bengkulu Malay and evaluating the quality of the machine translation output. The training and testing data consist of parallel corpora taken from Bengkulu Malay dictionaries and online resources for Indonesian corpora, with a total of 5261 parallel sentence pairs. Several steps are performed in SMT. The initial step is preprocessing, aimed at preparing the parallel corpus. After that, a training phase is conducted, where the parallel corpus is processed to build language and translation models. Subsequently, a testing phase is carried out, followed by an evaluation phase. Based on the research results, various factors influence the quality of SMT translation output. The most important factor is the quantity and quality of the parallel corpus used as the foundation for developing translation and language models. The machine translation output is automatically evaluated using the Bilingual Evaluation Understudy (BLEU), indicating accuracy values observed when using 500 sentences, 1500 sentences, 2500 sentences, 4000 sentences, and 5261 sentences are 80.56%, 90.86%, 92.50%, 92.91%, and 94.48% respectively.

Efficient incremental training using a novel NMT-SMT hybrid framework for translation of low-resource languages.

The data-hungry statistical machine translation (SMT) and neural machine translation (NMT) models offer state-of-the-art results for languages with abundant data resources. However, extensive research is imperative to make these models perform equally well for low-resource languages. This paper proposes a novel approach to integrate the best features of the NMT and SMT systems for improved translation performance of low-resource English-Tamil language pair. The suboptimal NMT model trained with the small parallel corpus translates the monolingual corpus and selects only the best translations, to retrain itself in the next iteration. The proposed method employs the SMT phrase-pair table to determine the best translations, based on the maximum match between the words of the phrase-pair dictionary and each of the individual translations. This repeating cycle of translation and retraining generates a large quasi-parallel corpus, thus making the NMT model more powerful. SMT-integrated incremental training demonstrates a substantial difference in translation performance as compared to the existing approaches for incremental training. The model is strengthened further by adopting a beam search decoding strategy to produce k best possible translations for each input sentence. Empirical findings prove that the proposed model with BLEU scores of 19.56 and 23.49 outperforms the baseline NMT with scores 11.06 and 17.06 for Eng-to-Tam and Tam-to-Eng translations, respectively. METEOR score evaluation further corroborates these results, proving the supremacy of the proposed model.

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 MT (WMT), British National Corpus (BNC), Gutenberg, Reuters Corpus, and OpenSubtitles corpus, and enhanced the data in the corpora. The improved NMT model was compared with traditional NMT models, Rule-Based MT (RBMT), and Statistical MT (SMT). The experimental results showed that the average verb semantic matching degree of the improved NMT model in five corpora was 0.85, and the average Bilingual Evaluation Understudy (BLEU) score in five 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.

English-Chinese Translation Quality Assessment Based on Phrase Statistical Machine Translation Decoding Algorithm

Machine learning translation is the automated process of translating text from one language to another using computational algorithms and statistical models. neural network-based approaches, particularly using models like sequence-to-sequence (Seq2Seq) with attention mechanisms, have shown remarkable performance improvements in translation quality. This paper proposes a Statistical Stochastic Gradient Machine Translation Decoding (SSGM-TD) algorithm for the English–Chinese translation for the quality assessment. The proposed SSGM-TD model uses statistical analysis for the estimation and evaluation of the features for the computation of variables. The proposed SSGM – TD model estimates the stochastic gradient with the regression analysis for the feature estimation. The developed SSGM-TD model is implemented with the machine learning model for the automated translation of the English–Chinese languages. The simulation analysis is performed for the evaluation of the quality assessment in the translation process. The detailed evaluation is conducted using various metrics, including BLEU and METEOR scores, offering quantitative insights into the algorithm's performance. The classification process of the SSGM-TD algorithm is examined, revealing its proficiency in correctly classifying positive and negative instances. Precision, recall, and F1 score metrics provide a significant evaluation of the algorithm's classification capabilities. The decoding results and quality assessments are presented with providing a comprehensive view of the algorithm's strengths and potential areas for improvement. The quality assessments incorporate both quantitative metrics and human evaluations, ensuring a holistic understanding of the algorithm's translation capabilities. The consistency between automated metrics and human assessments underscores the algorithm's commendable performance in maintaining semantic accuracy and linguistic coherence.

Revolutionising Translation Technology: A Comparative Study of Variant Transformer Models - BERT, GPT, and T5

Recently, transformer-based models have reshaped the landscape of Natural Language Processing (NLP), particularly in the domain of Machine Translation (MT). this study explores three revolutionary transformer models: Bidirectional Encoder Representations from Transformers (BERT), Generative Pretrained Transformer (GPT), and Text-to-Text Transfer Transformer (T5). The study delves into their architecture, capabilities, and applications in the context of translation technology. The study begins by discussing the evolution of machine translation from rule-based to statistical machine translation and finally to transformer models. The models have distinct architectures and purposes which pushed the limits of MT and have been instrumental in revolutionising the field. The study found significant contributions of the models in the advancement of NLP tasks including translation technology. Using comparative approach, the study further elaborates on each model’s design and utility. BERT is strong in excelling in tasks requiring a deep understanding of the context. GPT is excellent for tasks such as text generation, translation and creative writing. While the strengths of T5 is text-to-text framework by simplifying the taskspecific architectures, making it easy to perform different NLP tasks. Recognising these models’ unique features allows translators to select the best one for particular translation tasks and adjust them for better accuracy, fluency, and cultural relevance in translations. The study concludes that the models bridge language barriers, improve cross-cultural communication and pave way for more accurate and natural translations in the future. The study also points out that language processing models are continually evolving but understanding BERT, GPT, and T5’s specific features is key for ongoing development in translation technology.

Statistical machine translation for Indic languages

Abstract Statistical Machine Translation (SMT) systems use various probabilistic and statistical Natural Language Processing (NLP) methods to automatically translate from one language to another language while retaining the originality of the context. This paper aims to discuss the development of bilingual SMT models for translating English into fifteen low-resource Indic languages (ILs) and vice versa. The process to build the SMT model is described and explained using a workflow diagram. Samanantar and OPUS corpus are utilized for training, and Flores200 corpus is used for fine-tuning and testing purposes. The paper also highlights various preprocessing methods used to deal with corpus noise. The Moses open-source SMT toolkit is being investigated for the system’s development. The impact of distance-based reordering and Morpho-syntactic Descriptor Bidirectional Finite-State Encoder (msd-bidirectional-fe) reordering on ILs is compared in the paper. This paper provides a comparison of SMT models with Neural Machine Translation (NMT) for ILs. All the experiments assess the translation quality using standard metrics such as BiLingual Evaluation Understudy, Rank-based Intuitive Bilingual Evaluation Score, Translation Edit Rate, and Metric for Evaluation of Translation with Explicit Ordering. From the result, it is observed that msd-bidirectional-fe reordering performs better than the distance-based reordering model for ILs. It is also noticed that even though the IL-English and English-IL systems are trained using the same corpus, the former performs better for all the evaluation metrics. The comparison between SMT and NMT shows that across various languages, SMT performs better in some cases, while NMT outperforms in others.

From RNN techniques to pre-trained models: emphasis on the use in Arabic machine translation

<p>Neural Machine Translation (NMT) has gained increasing attention in recent years due to its promising performance over conventional approaches such as Statistical Machine Translation. Nevertheless, when applied to languages with different structures, such as the pair (English, Arabic) that interests us in this work, its efficiency is degraded.<br />Alternatively, the use of unsupervised pre-training of large neural models has recently made a significant leap forward in the field of Natural Language Processing (NLP). By warm starting from published checkpoints, NLP practitioners have been able to push the state-of-the-art boundaries on multiple benchmarks while simultaneously saving considerable computational time. The emphasis so far has been mainly on natural language understanding problems. In this paper, we show the efficacy of pre-trained checkpoints for Arabic Machine Translation. We have evolved a transformer-based sequence-to-sequence model which is compatible with pre-trained checkpoints publicly available for Arabic Bidirectional Encoder Representations from Transformers (AraBERT) and Arabic Generative Pre-trained Transformer (AraGPT) and conducted a thorough empirical study on the usefulness of initializing our Arabic MT model, both encoder and decoder, with these checkpoints. Our models yield new state-of-the-art results in Arabic MT.</p>

Automatic translation from English to Amazigh using transformer learning

Due to the lack of parallel data, to our knowledge, no study has been conducted on the Amazigh-English language pair, despite the numerous machine translation studies completed between major European language pairs. We decided to utilize the neural machine translation (NMT) method on a parallel corpus of 137,322 sentences. The attention-based encoder-decoder architecture is used to construct statistical machine translation (SMT) models based on Moses, as well as NMT models using long short-term memory (LSTM), gated recurrent units (GRU), and transformers. Various outcomes were obtained for each strategy after several simulations: 80.7% accuracy was achieved using the statistical approach, 85.2% with the GRU model, 87.9% with the LSTM model, and 91.37% with the transformer.

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.

An intelligent error correction model for English grammar with hybrid attention mechanism and RNN algorithm

Abstract This article proposes an English grammar intelligent error correction model based on the attention mechanism and Recurrent Neural Network (RNN) algorithm. It aims to improve the accuracy and effectiveness of error correction by combining the powerful context-capturing ability of the attention mechanism with the sequential modeling ability of RNN. First, based on the improvement of recurrent neural networks, a bidirectional gated recurrent network is added to form a dual encoder structure. The encoder is responsible for reading and understanding the input text, while the decoder is responsible for generating the corrected text. Second, the attention mechanism is introduced into the decoder to convert the output of the encoder into the attention probability distribution for integration. This allows the model to focus on the relevant input word as it generates each corrected word. The results of the study showed that the model was 2.35% points higher than statistical machine translation–neural machine translation in the CoNLL-2014 test set, and only 1.24 points lower than the human assessment score, almost close to the human assessment level. The model proposed in this study not only created a new way of English grammar error correction based on the attention mechanism and RNN algorithm in theory but also effectively improved the accuracy and efficiency of English grammar error correction in practice. It further provides English learners with higher-quality intelligent error correction tools, which can help them learn and improve their English level more effectively.

The use of residual analysis to improve the error rate accuracy of machine translation

The aim of the study is to compare two different approaches to machine translation—statistical and neural—using automatic MT metrics of error rate and residuals. We examined four available online MT systems (statistical Google Translate, neural Google Translate, and two European commission’s MT tools—statistical mt@ec and neural eTranslation) through their products (MT outputs). We propose using residual analysis to improve the accuracy of machine translation error rate. Residuals represent a new approach to comparing the quality of statistical and neural MT outputs. The study provides new insights into evaluating machine translation quality from English and German into Slovak through automatic error rate metrics. In the category of prediction and syntactic-semantic correlativeness, statistical MT showed a significantly higher error rate than neural MT. Conversely, in the category of lexical semantics, neural MT showed a significantly higher error rate than statistical MT. The results indicate that relying solely on the reference when determining MT quality is insufficient. However, when combined with residuals, it offers a more objective view of MT quality and facilitates the comparison of statistical MT and neural MT.

The Translation Surfaces on Statistical Manifolds with Normal Distribution

In this paper, we will investigate translation surfaces on statistical manifolds. Statistical manifolds are mathematical structures that describe the geometric properties of statistical models. We will focus on minimal statistical translation surfaces and then classify statistical translation surfaces of null sectional curvature in three-dimensional hyperbolic statistical manifolds

On the use of statistical machine translation for suggesting variable names for decompiled code: The Pharo case

Adequately selecting variable names is a difficult activity for practitioners. In 2018, Jaffe et al. proposed the use of statistical machine translation (SMT) to suggest descriptive variable names for decompiled code. A large corpus of decompiled C code was used to train the SMT model. Our paper presents the results of a partial replication of Jaffe’s experiment. We apply the same technique and methodology to a dataset made of code written in the Pharo programming language. We selected Pharo since its syntax is simple – it fits on half of a postcard – and because the optimizations performed by the compiler are limited to method scope. Our results indicate that SMT may recover between 8.9% and 69.88% of the variable names depending on the training set. Our replication concludes that: (i) the accuracy depends on the code similarity between the training and testing sets; (ii) the simplicity of the Pharo syntax and the satisfactory decompiled code alignment have a positive impact on predicting variable names; and (iii) a relatively small code corpus is sufficient to train the SMT model, which shows the applicability of the approach to less popular programming languages. Additionally, to assess SMT’s potential in improving original variable names, ten Pharo developers reviewed 400 SMT name suggestions, with four reviews per variable. Only 15 suggestions (3.75%) were unanimously viewed as improvements, while 45 (11.25%) were perceived as improvements by at least two reviewers, highlighting SMT’s limitations in providing suitable alternatives.

Machine translation literacy in the legal translation context: a SWOT analysis perspective

ABSTRACT In the past couple of decades, machine translation (MT), whether phrase-based statistical or neural MT, has made considerable progress and has become increasingly common in specialised translation workflows. More studies on MT in legal contexts have been conducted in the past several years, and MT has been and continues to be used in international and supranational institutions that rely on legal translation and whose voluminous collections of translated text have been used to train contemporary systems. The time is ripe for a synthesis of developments in the area that may reinforce our understanding of what we might expect from MT from the perspective of legal translators. This systematic review of legal translation and MT studies teases out potential strengths/weaknesses of MT and opportunities/threats of using it from a legal translator perspective, focusing on lexical and morphosyntactic challenges that may set legal translation apart from other specialised translation areas. A SWOT analysis from this perspective may offer value as a pedagogical framework for fostering domain-specific MT literacy in the legal translation classroom. Students will develop an understanding of what might reasonably be expected from MT in legal translation contexts, while reinforcing their understanding of legal translation challenges.

Novel algorithm machine translation for language translation tool

AbstractFuzzy matching techniques are the presently used methods in translating the words. Neural machine translation and statistical machine translation are the methods used in MT. In machine translator tool, the strategy employed for translation needs to handle large amount of datasets and therefore the performance in retrieving correct matching output can be affected. In order to improve the matching score of MT, the advanced techniques can be presented by modifying the existing fuzzy based translator and neural machine translator. The conventional process of modifying architectures and encoding schemes are tedious process. Similarly, the preprocessing of datasets also involves more time consumption and memory utilization. In this article, a new spider web based searching enhanced translation is presented to be employed with the neural machine translator. The proposed scheme enables deep searching of available dataset to detect the accurate matching result. In addition, the quality of translation is improved by presenting an optimal selection scheme for using the sentence matches in source augmentation. The matches retrieved using various matching scores are applied to an optimization algorithm. The source augmentation using optimal retrieved matches increases the translation quality. Further, the selection of optimal match combination helps to reduce time requirement, since it is not necessary to test all retrieved matches in finding target sentence. The performance of translation is validated by measuring the quality of translation using BLEU and METEOR scores. These two scores can be achieved for the TA‐EN language pairs in different configurations of about 92% and 86%, correspondingly. The results are evaluated and compared with other available NMT methods to validate the work.

Comparison of Adam Optimization and RMS prop in Minangkabau-Indonesian Bidirectional Translation with Neural Machine Translation

Language is a tool humans use to establish communication. Still, the language used is one language and between regions or nations with their languages. Indonesia is a country that has a diversity of second languages and is the fourth most populous country in the world. It is recorded that Indonesia has nearly 800 regional languages, but research activities in natural language processing are still lacking. Minangkabau is an endangered language spoken by the Minangkabau people in Indonesia's West Sumatra province. According to UNESCO, the Minangkabau language is listed as a language that is "definitely endangered," with only around 5 million speakers worldwide. This study uses neural machine translation (NMT) to create a formula based on this information. Neural machine translation, in contrast to conventional statistical machine translation, intends to build a single neural network that can be built up to achieve the best performance. Because it can simultaneously hold memory for a long time, comprehend complicated relationships in data, and provide information that is very important in determining the outcome of translation, LSTM is one of the most powerful machine-learning techniques for translating languages. The BLUE score is utilized in the NMT evaluation. The test results use 520 Minangkabau sentences, conducting tests based on the number of epochs ranging from 100-1000, resulting in optimization using Adam being better than optimization RMSprop. This is evidenced by the results of the best BLUE-1 score of 0.997816 using 1000 epochs.

Correction to: Improving paraphrase generation using supervised neural-based statistical machine translation framework

Correction to: Improving paraphrase generation using supervised neural-based statistical machine translation framework

Consensus-Based Machine Translation for Code-Mixed Texts

Multilingualism in India is widespread due to its long history of foreign acquaintances. This leads to the presence of an audience familiar with conversing using more than one language. Additionally, due to the social media boom, the usage of multiple languages to communicate has become extensive. Hence, the need for a translation system that can serve the novice and monolingual user is the need of the hour. Such translation systems can be developed by methods such as statistical machine translation and neural machine translation, where each approach has its advantages as well as disadvantages. In addition, the parallel corpus needed to build a translation system, with code-mixed data, is not readily available. In the present work, we present two translation frameworks that can leverage the individual advantages of these pre-existing approaches by building an ensemble model that takes a consensus of the final outputs of the preceding approaches and generates the target output. The developed models were used for translating English-Bengali code-mixed data (written in Roman script) into their equivalent monolingual Bengali instances. A code-mixed to monolingual parallel corpus was also developed to train the preceding systems. Empirical results show improved BLEU and TER scores of 17.23 and 53.18 and 19.12 and 51.29, respectively, for the developed frameworks.

The Integration of Machine Translation Technology in the Realm of Legal Interpretation

This article aims to provide an overview of the application of machine translation technology in the field of legal translation, exploring its potential, challenges, and future directions. Firstly, it reviews the development process of machine translation technology, including rule-based methods, statistical machine translation, and the application of deep learning methods. Secondly, on the basis of in-depth deconstruction of the operating rules of the big language model, it is demonstrated that the highly modeled written legal language is highly consistent with the underlying logic of the big language model because of its standardization, accuracy and de-contextualization, so compared with other styles, machine translation technology will achieve better translation results in the field of legal translation. In addition, multimodal technology can also be applied to court interpretation, which greatly alleviates the shortage of qualified interpreters and maintains judicial justice. Furthermore, it discusses the human-machine collaborative model in legal translation, emphasizing the importance of human proofreading and review in ensuring translation accuracy and reliability. Lastly, it summarizes the prospects and challenges of machine translation technology in the field of legal translation. Through a systematic review and analysis of relevant literature, this article reveals the immense potential of machine translation technology in legal translation. It can significantly enhance the efficiency and quality of legal translation, thereby enhancing the capacity of legal language services.