A Survey of Orthographic Information in Machine Translation

Chakravarthi, Bharathi Raja; Rani, Priya; Arčan, Mihael; McCrae, John P.

doi:10.1007/s42979-021-00723-4

Cited by 12 publications

(9 citation statements)

References 104 publications

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“…According to incomplete statistics, there are now around 7,000 human languages [ 1 ]. Most of the current machine translation technology is based on big data.…”

Section: Introductionmentioning

confidence: 99%

“…Only by training on a large amount of data can we get a better effect. In fact, only a few languages such as Chinese, English, French, and German have more training data, and there are almost no training resources available for other languages [ 1 ]. In some specialized fields, such as marine science and technology, there are fewer resources in Chinese and other languages.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Chinese Machine Translation Training Based on Deep Learning Technology

Sun

2022

Computational Intelligence and Neuroscience

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With the advent of the information age, people can establish good communication through Internet technology. Mechanical translation has become a key means to solve people’s communication problems. However, there are still obstacles to communication between different languages. In order to solve this problem, this paper uses existing neural network technology to the English-Chinese bidirectional machine translation model in the field of marine science and technology. Based on deep learning technology, we collect Chinese and English abstracts and partial full texts of Chinese and English papers with marine science and technology as the key words and build a professional corpus in English and Chinese about marine science and technology. In the Chinese-English bidirectional translation model, the local weight sharing is introduced into the Chinese encoder and the English encoder, and the output of the Chinese encoder sublayer and the English encoder sublayer is fused as the output of the respective encoders, and the performance of the translation model is evaluated using the BLEU parameters. Through the training of the translation model, compared with the transformer model, the BLEU value of the model with local weight sharing and encoder sublayer fusion output is improved by 1.6 and 3.8 in the Chinese-English and English-Chinese translation directions, respectively. The PPL values in the Chinese-English and English-Chinese translation directions decreased by 18.72% and 14.62%, respectively. We demonstrate the effectiveness of the language translation model. Experiments show that the research on machine language adaptive technology based on deep learning can more smoothly realize the two-way translation of literature in the field of marine science and technology. Compared with traditional mechanical translation, this paper proposes a translation model based on the deep neural algorithm, which improves the effect of model training by constructing a Chinese-English corpus with the theme of marine science and technology.

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“…According to incomplete statistics, there are now around 7,000 human languages [ 1 ]. Most of the current machine translation technology is based on big data.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of Chinese Machine Translation Training Based on Deep Learning Technology

Sun

2022

Computational Intelligence and Neuroscience

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“…The performance of NMT schemes even in the case of noisy data could be improved with Fuzzy match retrieval method combined with source-target concatenation. 42 In works, [43][44][45][46][47][48][49][50] CAT tools for translating into Indian languages were presented. In Reference 50, the authors presented an open source and extendable Morphological Analyser cum Generator (MAG) for Tamil language.…”

Section: Related Workmentioning

confidence: 99%

Novel algorithm machine translation for language translation tool

Jayasakthi Velmurugan,

Sumathy,

Pradeep

2024

Computational Intelligence

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Fuzzy 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.

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“…This notation (like many other similar notations) can project all the Indic or Brahmi origin scripts [40], which have -in many 1 Using encoding converters, such as https://pypi.org/project/ wxconv/ cases -different Unicode blocks, into a common character space. Our intuition, is that this should help in capturing phonological, orthographic, and, to some extent, morphosyntactic similarities that will help a neural network-based model in better multilingual learning and translation across this languages [38,39,67]. We do this by using this WX-converted text to learn byte pair encoding-based embeddings.…”

Section: Introductionmentioning

confidence: 99%

Study of Local Site Conditions for Seismic Hazard Assessment at the BHU Campus, Varanasi, North India, using Ambient Noise Measurements

Kumari

Singh

Shankar

et al. 2022

Journal of the Geological Society of India

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The use of subword embedding has proved to be a major innovation in Neural Machine Translation (NMT). It helps NMT to learn better context vectors for Low Resource Languages (LRLs) so as to predict the target words by better modelling the morphologies of the two languages and also the morphosyntax transfer. Even so, their performance for translation in Indian language to Indian language scenario is still not as good as for resource-rich languages. One reason for this is the relative morphological richness of Indian languages, while another is that most of them fall into the extremely low resource or zero-shot categories. Since most major Indian languages use Indic or Brahmi origin scripts, the text written in them is highly phonetic in nature and phonetically similar in terms of abstract letters and their arrangements. We use these characteristics of Indian languages and their scripts to propose an approach based on common multilingual Latin-based encodings (WX notation) that take advantage of language similarity while addressing the morphological complexity issue in NMT. These multilingual Latin-based encodings in NMT, together with Byte Pair Embedding (BPE) allow us to better exploit their phonetic and orthographic as well as lexical similarities to improve the translation quality by projecting different but similar languages on the same orthographic-phonetic character space. We verify the proposed approach by demonstrating experiments on similar language pairs (Gujarati↔Hindi, Marathi↔Hindi, Nepali↔Hindi, Maithili↔Hindi, Punjabi↔Hindi, and Urdu↔Hindi) under low resource conditions. The proposed approach shows an improvement in a majority of cases, in one case as much as ∼10 BLEU points compared to baseline techniques for similar language pairs. We also get up to ∼1 BLEU points improvement on distant and zero-shot language pairs.

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A Survey of Orthographic Information in Machine Translation

Cited by 12 publications

References 104 publications

Analysis of Chinese Machine Translation Training Based on Deep Learning Technology

Analysis of Chinese Machine Translation Training Based on Deep Learning Technology

Novel algorithm machine translation for language translation tool

Study of Local Site Conditions for Seismic Hazard Assessment at the BHU Campus, Varanasi, North India, using Ambient Noise Measurements

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