Performance Of Machine Translation Research Articles

Analysis of Address Terms Translation Strategies: The Norms in the Translation of Address Terms in Moment in Peking

This paper, framed within Chestermans translation ethics, focuses on the translation of address terms in the first volume of Moment in Peking. It analyzes the practical application of professional norms and expected norms in the translation of novels, exploring the strategies of address terms translation. Through comparative analysis of different Chinese translations of address terms in the first volume of Moment in Peking (translated by Zhang Zhenyu, Yu Fei, and machine), this paper aims to reveal the ethical and professional norms that translators should consider in the selection of address terms and explore the performance of machine translation in terms of norms. The study finds that the ethical principles of human translators result in higher quality and reliability of translations, while machine translation, although formally faithful, still has deficiencies in context perception, emotional discernment, and target reader cognition.

Comparative assessment of Bing Translator and Youdao Machine Translation Systems in English-to-Chinese literary text translation

This study explores the performance of machine translation of literary texts from English to Chinese. The study compares two machine translation systems, Bing Translator and Youdao Machine Translation, using selected texts from the novel “Nineteen eighty-four” by George Orwell. The data collection includes the original source texts, their machine-generated translations by Bing Translator and Youdao Machine Translation, and comparisons with human reference translations to assess the performance of these systems. The research’s focal point is to evaluate the accuracy, fluency, and appropriateness of translations generated by these two machine translation systems, while also analyzing the post-editing effort required to enhance the quality of the final machine-translated product. The study revealed that despite the presence of flaws in both machine translation systems, Youdao Machine Translation demonstrated superior performance, especially in accurately translating technical terms and idiomatic expressions, making it the more effective option overall. Nevertheless, the translations from Youdao Machine Translation required more substantial post-editing efforts to improve fluency and readability. Conversely, Bing Translator yielded more fluent and natural-sounding translations, albeit with a need for improved accuracy in translating technical terms and idiomatic expressions. The study concludes that while machine translation systems are capable of generating reasonable translations for literary texts, human post-editing remains essential to ensure the final output’s accuracy, fluency, and appropriateness. The study underscores the importance of selecting the appropriate machine translation system based on the nature of the text being translated. It also highlights the critical role of post-editing in refining the quality of machine-translated outputs, suggesting that while machine translation can provide a solid foundation, human intervention is indispensable for achieving optimal accuracy, fluency, and overall readability in literary translations.

A Study for Enhancing Low-resource Thai-Myanmar-English Neural Machine Translation

Several methodologies have recently been proposed to enhance the performance of low-resource Neural Machine Translation (NMT). However, these techniques have yet to be explored thoroughly in the low-resource Thai and Myanmar languages. Therefore, we first applied augmentation techniques such as SwitchOut and Ciphertext Based Data Augmentation (CipherDAug) to improve NMT performance in these languages. Second, we enhanced the NMT performance by fine-tuning the pre-trained Multilingual Denoising BART model (mBART), where BART denotes Bidirectional and Auto-Regressive Transformer. We implemented three NMT systems: namely, Transformer+SwitchOut, Multi-Source Transformer+CipherDAug, and fine-tuned mBART in the bidirectional translations of Thai-English-Myanmar language pairs from the ASEAN-MT corpus. Experimental results showed that Multi-Source Transformer+CipherDAug significantly improved Bilingual Evaluation Understudy (BLEU), Character n-gram F-score (ChrF) , and Translation Error Rate (TER) scores over the first baseline Transformer and second baseline Edit-Based Transformer. The model achieved notable BLEU scores: 37.9 (English-to-Thai), 42.7 (Thai-to-English), 28.9 (English-to-Myanmar), 31.2 (Myanmar-to-English), 25.3 (Thai-to-Myanmar), and 25.5 (Myanmar-to-Thai). The fine-tuned mBART model also considerably outperformed the two baselines, except for the Myanmar-to-English pair. SwitchOut improved over the second baseline in all pairs and performed similarly to the first baseline in most cases. Last, we performed detailed analyses verifying that the CipherDAug and mBART models potentially facilitate improving low-resource NMT performance in Thai and Myanmar languages.

Towards Understanding Neural Machine Translation with Attention Heads’ Importance

Although neural machine translation has made great progress, and the Transformer has advanced the state-of-the-art in various language pairs, the decision-making process of the attention mechanism, a crucial component of the Transformer, remains unclear. In this paper, we propose to understand the model’s decisions by the attention heads’ importance. We explore the knowledge acquired by the attention heads, elucidating the decision-making process through the lens of linguistic understanding. Specifically, we quantify the importance of each attention head by assessing its contribution to neural machine translation performance, employing a Masking Attention Heads approach. We evaluate the method and investigate the distribution of attention heads’ importance, as well as its correlation with part-of-speech contribution. To understand the diverse decisions made by attention heads, we concentrate on analyzing multi-granularity linguistic knowledge. Our findings indicate that specialized heads play a crucial role in learning linguistics. By retaining important attention heads and removing the unimportant ones, we can optimize the attention mechanism. This optimization leads to a reduction in the number of model parameters and an increase in the model’s speed. Moreover, by leveraging the connection between attention heads and multi-granular linguistic knowledge, we can enhance the model’s interpretability. Consequently, our research provides valuable insights for the design of improved NMT models.

Handling syntactic difference in Chinese-Vietnamese neural machine translation

As the typical distant language pair, Chinese and Vietnamese vary widely in syntactic structure, which significantly influences the performance of Chinese-Vietnamese machine translation. To address this problem, we present a simple approach with a pre-reordering model for closing syntactic gaps of the Chinese-Vietnamese language pair. Specifically, we first propose an algorithm for recognizing the modifier inverse, one of the most representative syntactic different in Chinese-Vietnamese language pair. Then we pre-train a pre-reordering model based on the former recognition algorithm and incorporate it into the attention-based translation framework for syntactic different reordering. We conduct empirical studies on Chinese-Vietnamese neural machine translation task, the results show that our approach achieves average improvement of 2.75 BLEU points in translation quality over the baseline model. In addition, the translation fluency can be significantly improved by over 2.44 RIBES points.

A study on intelligent translation of English sentences by a semantic feature extractor

Abstract In order to enhance the performance of machine translation, this article briefly introduced algorithms that can be used to extract semantic feature vectors. Then, the aforementioned algorithms were integrated with the encoder–decoder translation algorithm, and the resulting algorithms were subsequently tested. First, the performance of the semantic recognition of the long short-term memory (LSTM)-based semantic feature extractor was tested, followed by a comparison with the translation algorithm that does not include semantic features, as well as the translation algorithm that incorporates convolutional neural network-extracted semantic features. The findings demonstrated that the LSTM-based semantic feature extractor accurately identified the semantics of the source language. The proposed translation algorithm, which is based on LSTM semantic features, achieved more accurate translations compared to the other two algorithms. Furthermore, it was less affected by the length of the source language.

Progressive modality-complement aggregative multitransformer for domain multi-modal neural machine translation

Domain-specific Multi-modal Neural Machine Translation (DMNMT) aims to translate domain-specific sentences from a source language to a target language by incorporating text-related visual information. Generally, domain-specific text-image data often complement each other and have the potential to collaboratively enhance the representation of domain-specific information. Unfortunately, there is a considerable modality gap between image and text in data format and semantic expression, which leads to distinctive challenges in domain-text translation tasks. Narrowing the modality gap and improving domain-aware representation are two critical challenges in DMNMT. To this end, this paper proposes a progressive modality-complement aggregative MultiTransformer, which aims to simultaneously narrow the modality gap and capture domain-specific multi-modal representation. We first adopt a bidirectional progressive cross-modal interactive strategy to effectively narrow the text-to-text, text-to-visual, and visual-to-text semantics in the multi-modal representation space by integrating visual and text information layer-by-layer. Subsequently, we introduce a modality-complement MultiTransformer based on progressive cross-modal interaction to extract the domain-related multi-modal representation, thereby enhancing machine translation performance. Experiment results on the Fashion-MMT and Multi-30k datasets are conducted, and the results show that the proposed approach outperforms the compared state-of-the-art (SOTA) methods on the En-Zh task in E-commerce domain, En-De, En-Fr and En-Cs tasks of Multi-30k in general domain. The in-depth analysis confirms the validity of the proposed modality-complement MultiTransformer and bidirectional progressive cross-modal interactive strategy for DMNMT.

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.

LANGUAGE IDENTIFICATION FOR MULTILINGUAL MACHINE

Language Identification for Multilingual Machine Translation is a crucial component in modern natural language processing systems, enabling accurate and efficient translation across multiple languages. This paper presents a comprehensive approach to language identification that enhances the performance of multilingual machine translation systems.The proposed method utilizes advanced machine learning techniques to automatically detect the language of a given text with high accuracy. By incorporating a variety of linguistic features and leveraging large-scale multilingual datasets, the system can identify languages even in challenging scenarios such as code-switching and mixed-language inputs.Key contributions of this work include the development of a robust language identification model that integrates seamlessly with machine translation pipelines. The model is evaluated on diverse datasets, demonstrating its effectiveness in real-world applications. Additionally, we explore the impact of accurate language identification on the overall quality of machine translation, highlighting improvements in translation accuracy and fluency.

Filtered data augmentation approach based on model competence evaluation

The scale of parallel corpus plays an important role in training high-quality neural machine translation models. In order to expand the scale of parallel corpus in low-resource scenarios, researchers have proposed a series of data augmentation approaches, in which the most representative work is the back-translation. The back-translation approach uses a basic translation model to translate the target monolingual corpus into the source language, and then combines it into a pseudo parallel corpus to expand the training data. Due to the simple and efficient data expansion strategies, the back-translation approach becomes the mainstream data augmentation approach of neural machine translation. However, in low-resource scenarios, only low-precision basic translation models can be used. The pseudo-parallel corpus translated by this model contains low-quality sentence pairs, which inevitably introduces noises and leads to negative impacts to translation procedure. To improve the performance of low-resource neural machine translation, we propose a filtered data augmentation method based on the model competence evaluation, which can effectively improve the quality of training data by using both the filtered back-translation and dynamic evaluation of translation model capabilities. Comparative experiments show that the proposed approach can improve the quality of training data and the performance of low-resource neural machine translation.

A Chinese–Kazakh Translation Method That Combines Data Augmentation and R-Drop Regularization

Low-resource languages often face the problem of insufficient data, which leads to poor quality in machine translation. One approach to address this issue is data augmentation. Data augmentation involves creating new data by transforming existing data through methods such as flipping, cropping, rotating, and adding noise. Traditionally, pseudo-parallel corpora are generated by randomly replacing words in low-resource language machine translation. However, this method can introduce ambiguity, as the same word may have different meanings in different contexts. This study proposes a new approach for low-resource language machine translation, which involves generating pseudo-parallel corpora by replacing phrases. The performance of this approach is compared with other data augmentation methods, and it is observed that combining it with other data augmentation methods further improves performance. To enhance the robustness of the model, R-Drop regularization is also used. R-Drop is an effective method for improving the quality of machine translation. The proposed method was tested on Chinese–Kazakh (Arabic script) translation tasks, resulting in performance improvements of 4.99 and 7.7 for Chinese-to-Kazakh and Kazakh-to-Chinese translations, respectively. By combining the generation of pseudo-parallel corpora through phrase replacement with the application of R-Drop regularization, there is a significant advancement in machine translation performance for low-resource languages.

Neural Machine Translation of Electrical Engineering with Fusion of Memory Information

This paper proposes a new neural machine translation model of electrical engineering that combines a transformer with gated recurrent unit (GRU) networks. By fusing global information and memory information, the model effectively improves the performance of low-resource neural machine translation. Unlike traditional transformers, our proposed model includes two different encoders: one is the global information encoder, which focuses on contextual information, and the other is the memory encoder, which is responsible for capturing recurrent memory information. The model with these two types of attention can encode both global and memory information and learn richer semantic knowledge. Because transformers require global attention calculation for each word position, the time and space complexity are both squared with the length of the source language sequence. When the length of the source language sequence becomes too long, the performance of the transformer will sharply decline. Therefore, we propose a memory information encoder based on the GRU to improve this drawback. The model proposed in this paper has a maximum improvement of 2.04 BLEU points over the baseline model in the field of electrical engineering with low resources.

Analysis of Machine Translation Performance on Translating Informative Text from English into Indonesian

Analysis of Machine Translation Performance on Translating Informative Text from English into Indonesian

Effect of Diacritics on Machine Translation Performance: A Case Study of Yemeni Literature

Many Arabic texts are written without diacritics. However, in some contexts this raises the high level of homography and in turn presents difficulties for machine translation programs. Homographs are words which are spelled identically but have different meanings and are mostly pronounced differently. To avoid the problem of homography, words require to be diacriticized. Thus, the main objective of the study is to assess the ability of machine translation (henceforth MT) in rendering diacritical words from Arabic into English with special reference to translating Yemeni literature into English. This study will also compare the translations of three MT programs, namely, (Reverso, Systran Translate and Free Translation Online) to find out which program is close to the original meaning of the source language texts. Further, the study aims to identify some causes that stand behind errors of translating diacriticized words that result from the mentioned programs. To achieve these aims, descriptive, analytical and comparative methods were followed by the researcher. Thus, the three common and modern MT programs, Reverso, Systran and Free Translation Online were selected to translate some diacriticized words. Then, some excerpts with their contexts were taken from the two Yemeni works, The Hostage (Ar-rahinah) (???????) by the Yemeni famous writer Zayd Muttee Dammaj and the Yemeni book Yemeni Wealth from Popular Proverbs ?????? ??????? ?? ??????? ??????? by the Yemeni writer Muhammad Al-Adimi. The chosen samples were inserted into these MT programs to be electronically translated and then analyzed and discussed qualitatively and quantitatively. The study has concluded that MT encountered the problem of diacritics in Arabic texts; as a result most of the time MT programs failed in recognizing diacritics on letters. Thus, most of the programs' translation results were incorrect and did not concord with the original meaning. It can also be concluded that Free Translation Online program produced the least errors of the three programs and Systran mistranslated all the diacriticized excerpts. These errors can be attributed to absence of programs which contain the diacritic system of Arabic.

Adding visual attention into encoder-decoder model for multi-modal machine translation

Multi-modal neural machine translation (MNMT) aims to integrate visual and textual information to translate source sentences into target and attracts a lot of attentions. Existing methods contribute a lot for capturing interactions between visual and textual features to improve the performance of neural machine translation (NMT). However, most of them don't consider the multi-modal consistency for MNMT. In fact, the image provides the semantic global consistency between different languages. We believe that adding bilingual-visual agreement into the encoder and decoder simultaneously can obtain bilingual representations and is useful for NMT. In this paper, we propose to simultaneously integrate visual information in encoder and decoder to learn the interactions between visual and textual features, in this paper called the model VMNMT. As the visual information provide global context, the encoder and decoder can learn the bilingual representations. Besides, we introduce a new bilingual-visual agreement decoder to learn to better representations of corresponding image-sentence pairs. In the experiment, the improvement was 2.02 BLEU on the English-German16 dataset and 1.9 BLEU on the English-German17 dataset. The results show that our method can outperform baselines on several widely-used datasets in terms of various metrics.

Unsupervised Parallel Sentences of Machine Translation for Asian Language Pairs

Parallel sentence pairs play a very important role in many natural language processing tasks, especially cross-lingual tasks such as machine translation. So far, many Asian language pairs lack bilingual parallel sentences. As collecting bilingual parallel data is very time-consuming and difficult, it is very important for many low-resource Asian language pairs. While existing methods have shown encouraging results, they rely on bilingual data seriously or have some drawbacks in an unsupervised situation. To address these issues, we propose a new unsupervised similarity calculation and dynamic selection metric to obtain parallel sentence pairs in an unsupervised situation. First, our method maps bilingual word embedding by postdoc adversarial training, which rotates the source space to match the target without parallel data. Then, we introduce a new cross-domain similarity adaption to obtain parallel sentence pairs. Experimental results on real-world datasets show that our model can obtain better accuracy and recall on mining parallel sentence pairs. We also show that the extracted bilingual sentence corpora can significantly improve the performance of neural machine translation.

Deep Learning in Computational Linguistics for Chinese Language Translation

Applying artificial intelligence to Chinese language translation in computational linguistics is of practical significance for economic boosts and cultural exchanges. In the present work, the bi-directional long short-term memory (BiLSTM) network is employed to extract Chinese text features regarding the overlapping semantic roles in Chinese language translation and hard-to-converge training of high-dimensional text word vectors in text classification during translation. In addition, AlexNet is optimized to extract the local features of the text and meanwhile update and learn network parameters in the deep network. Then, the attention mechanism is introduced to build a forecasting algorithm of Chinese language translation based on BiLSTM and improved AlexNet. Last, the forecasting algorithm is simulated to validate its performance. Some state-of-the-art algorithms are selected for a comparative experiment, including long short-term memory, regions with convolutional neural network features, AlexNet, and support vector machine. Results demonstrate that the forecasting algorithm proposed here can achieve a feature identification accuracy of 90.55%, at least an improvement of 4.24% over other algorithms. In addition, it provides an area under the curve of above 90%, a training duration of about 54.21 seconds, and a test duration of about 19.07 seconds. Regarding the performance of Chinese language translation, the algorithm proposed here provides a bilingual evaluation understudy (BLEU) value of 28.21 on the training set, with a performance gain ratio reaching 111.55%; on the test set, its BLEU reaches 40.45, with a performance gain ratio of 129.80%. Hence, this forecasting algorithm is notably superior to other algorithms, which can enhance the machine translation performance. Through experiments, the Chinese language translation algorithm constructed here improves translation performance while ensuring a high correct identification rate, providing experimental references for the later intelligent development of Chinese language translation in computational linguistics.

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.

The Influence of Word Vectorization for Kawi Language to Indonesian Language Neural Machine Translation

People relatively use machine translation to learn any textual knowledge beyond their native language. There is already robust machine translation such as Google translate. However, the language list has only covered the high resource language such as English, France, etc., but not for Kawi Language as one of the local languages used in Bali's old works of literature. Therefore, it is necessary to study the development of machine translation from the Kawi language to the more active user language such as the Indonesian language to make easier learning access for the young learner. The research developed the neural machine translation (NMT) using recurrent neural network (RNN) based neural models and analyzed the influence of word vectorization using Word2Vec for the machine translation performance based on BLEU scores. The result shows that word vectorization indeed significantly increases the NMT models performance, and Long-Short Term Memory (LSTM) with attention mechanism has the highest BLEU scores equal to 20.86. The NMT models still could not achieve the BLEU scores on par with those human experts and high resource language machine translation. On the other hand, this initial study could be the reference for the future development of Kawi to Indonesian NMT.

Machine Translation of Scheduling Joint Optimization Algorithm in Japanese Passive Statistics

Machine translation is different from written translation. How to improve the performance of machine translation has been a research hotspot in current research on machine translation. In this paper, based on the semantic analysis and research of Japanese passive, a joint optimization algorithm of scheduling has been proposed, and the machine translation of Japanese passive has been studied. At present, machine translation is more and more widely used. Machine translation has solved many vocabulary problems, and it can complete a large amount of translation work and save a lot of manual translation time. While improving the translation speed, in the process of Japanese passive translation, it is also found that direct machine translation shows many shortcomings, and the quality of passive translation is not particularly ideal, exposing the basic problems of machine translation, such as semantic errors, syntactic errors, unclear and rigid expressions, and messy structures. In response to the problems above, this paper has improved the machine translation model for scheduling joint optimization algorithms. The paper has proposed several optimization algorithms and used resource awareness and computing power scheduling algorithms to conduct experimental analysis of translation performance. Finally, it is found that, among the two scheduling optimization algorithms, the resource-aware scheduling algorithm has better performance. With the same data, the resource-aware scheduling algorithm has saved 15.5% of the time compared with the computing power scheduling algorithm, and the accuracy of Japanese passive translation was 6%, 5%, and 21% higher than the computing power scheduling algorithm under different data volumes. Not only has the time taken been shortened, but the translation accuracy has also been improved.