scholarly journals Applying Machine Translation Methods in the Problem of Automatic Text Correction

2021 ◽  
Author(s):  
Wojciech Jarmosz
Keyword(s):  
Machine Translation ◽  
Translation Methods ◽  
Text Correction ◽  
Automatic Text

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

scholarly journals The Second QALB Shared Task on Automatic Text Correction for Arabic

2015 ◽  
Author(s):  
Alla Rozovskaya ◽  
Houda Bouamor ◽  
Nizar Habash ◽  
Wajdi Zaghouani ◽  
Ossama Obeid ◽  
...  
Keyword(s):  
Shared Task ◽  
Text Correction ◽  
Automatic Text

scholarly journals A Study of Neural Machine Translation from Chinese to Urdu

2020 ◽  
Vol 2 (4) ◽  
pp. 28
Author(s):  
. Zeeshan
Keyword(s):  
Machine Translation ◽  
Chinese Language ◽  
Language Translation ◽  
Target Language ◽  
Foreign Languages ◽  
Neural Machine Translation ◽  
Source Language ◽  
Great Progress ◽  
Score Method ◽  
Translation Methods

Machine Translation (MT) is used for giving a translation from a source language to a target language. Machine translation simply translates text or speech from one language to another language, but this process is not sufficient to give the perfect translation of a text due to the requirement of identification of whole expressions and their direct counterparts. Neural Machine Translation (NMT) is one of the most standard machine translation methods, which has made great progress in the recent years especially in non-universal languages. However, local language translation software for other foreign languages is limited and needs improving. In this paper, the Chinese language is translated to the Urdu language with the help of Open Neural Machine Translation (OpenNMT) in Deep Learning. Firstly, a Chineseto Urdu language sentences datasets were established and supported with Seven million sentences. After that, these datasets were trained by using the Open Neural Machine Translation (OpenNMT) method. At the final stage, the translation was compared to the desired translation with the help of the Bleu Score Method.

Accuracy analysis of Japanese machine translation based on machine learning and image feature retrieval

2020 ◽  
pp. 1-12
Author(s):  
Gang Song
Keyword(s):  
Machine Learning ◽  
Machine Translation ◽  
Character Recognition ◽  
Image Data ◽  
Image Features ◽  
Image Feature ◽  
Learning Technology ◽  
Japanese Character ◽  
Knowledge Support ◽  
Translation Methods

At present, there are still many deficiencies in Chinese-Japanese machine translation methods, the processing of corpus information is not deep enough, and the translation process lacks rich language knowledge support. In particular, the recognition accuracy of Japanese characters is not high. Based on machine learning technology, this study combines image feature retrieval technology to construct a Japanese character recognition model and uses Japanese character features as the algorithm recognition object. Moreover, this study expands image features by generating a brightness enhancement function using a bilateral grid. In order to exclude the influence of the edge and contour of the image scene on the analysis of the image source, the brightness value of the HDR image is used instead of the pixel value of the image as the image data. In addition, this research designs experiments to study the translation effects of this research model. The research results show that the model proposed in this paper has certain effects and can provide theoretical references for subsequent related research.

scholarly journals Automatic Text Correction for Devanagari OCR

2016 ◽  
Vol 9 (45) ◽  
Author(s):  
Atul Kumar ◽  
Gurpreet Singh Lehal ◽  
Gurpreet Singh Lehal
Keyword(s):  
Text Correction ◽  
Automatic Text

scholarly journals A Study of Statistical Machine Translation Methods for Under Resourced Languages

2016 ◽  
Vol 81 ◽  
pp. 250-257 ◽  
Author(s):  
Win Pa Pa ◽  
Ye Kyaw Thu ◽  
Andrew Finch ◽  
Eiichiro Sumita
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Translation Methods

scholarly journals Design and Testing of Automatic Machine Translation System Based on Chinese-English Phrase Translation

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Jing Ning ◽  
Haidong Ban
Keyword(s):  
Machine Translation ◽  
Language Processing ◽  
Evaluation System ◽  
Large Scale ◽  
Automatic Machine ◽  
Translation System ◽  
Automatic Translation ◽  
Translation Methods ◽  
Translation Systems ◽  
Design And Testing

With the development of linguistics and the improvement of computer performance, the effect of machine translation is getting better and better, and it is widely used. The automatic expression translation method based on the Chinese-English machine takes short sentences as the basic translation unit and makes full use of the order of short sentences. Compared with word-based statistical machine translation methods, the effect is greatly improved. The performance of machine translation is constantly improving. This article aims to study the design of phrase-based automatic machine translation systems by introducing machine translation methods and Chinese-English phrase translation, explore the design and testing of machine automatic translation systems based on the combination of Chinese-English phrase translation, and explain the role of machine automatic translation in promoting the development of translation. In this article, through the combination of machine translation experiments and machine automatic translation system design methods, the design and testing of machine automatic translation systems based on Chinese-English phrase translation combinations are studied to cultivate people's understanding of language, knowledge, and intelligence and then help solve other problems. Language processing issues promote the development of corpus linguistics. The experimental results in this article show that when the Chinese-English phrase translation probability table is changed from 82% to 51%, the BLEU translation evaluation system for the combination of Chinese-English phrases is improved. Automatic machine translation saves time and energy of translation work, which shows that machine translation shows its advantages due to its short development cycle and easy processing of large-scale corpora.

Lexical Simplification by Unsupervised Machine Translation

2020 ◽  
Vol 30 (02) ◽  
pp. 2050008
Author(s):  
Akihiro Katsuta ◽  
Kazuhide Yamamoto
Keyword(s):  
Information Transmission ◽  
Unsupervised Learning ◽  
Machine Translation ◽  
Statistical Machine Translation ◽  
Text Corpus ◽  
Parallel Corpus ◽  
Plain Text ◽  
Original Meaning ◽  
Text Simplification ◽  
Automatic Text

In recent years, simple Japanese has been attracting attention as information transmission for foreigners. Automatic text simplification aims to reduce the complexity of vocabulary and expressions in a sentence while retaining its original meaning. This paper aims at compressing vocabulary, focusing on lexical simplification. Since the construction or expansion of a simplification corpus is very costly, we construct a simplification model by unsupervised learning that does not require a parallel corpus for simplification. We construct a simplification model that does not require a parallel corpus using Unsupervised Statistical Machine Translation. Based on a predetermined vocabulary, a pseudo-corpus for simplification is constructed from a web corpus and we learn the simplification model by the pseudo-corpus. We only need a vocabulary and a plain text corpus to train the simplification model. Moreover, we propose to clean the phrase table by WordNet, which improves the performance in BLEU and SARI metrics. By suppressing distant paraphrasing with WordNet, it became easier to select the correct paraphrase candidate.

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