scholarly journals Language Statistical Machine Translation Muna to Indonesia Language

2021 ◽  
Vol 8 (4) ◽  
pp. 2173-2186
Author(s):  
Quranul Alfahrezi Agigi
Keyword(s):  
Machine Translation ◽  
Technological Development ◽  
Statistical Machine Translation ◽  
The Internet ◽  
Parallel Corpus ◽  
Translation Machine ◽  
Regional Languages

In this rapid technological development, there are still at least some machine translators from regional languages ​​to Indonesian. Therefore, this paper discusses to make a statistical translation machine for the Muna language into Indonesian because at least there are still at least a Muna translation machine into Indonesian. The approach used a statistically based using parallel corpus. In this study, the data taken came from a book entitled Folklore of Buton and Muna in Southeast Sulawesi and several folklore articles on the internet. The number of parallel corpus used is 1050 sentence lines and the monolingual corpus is 1351 sentence lines. The scenarios that will be carried out in this experiment are divided into two scenarios. Scenario 1 is testing on the parallel corpus (training) which is tested using the available sentence lines and these sentence lines will be added to each experiment, while the rest of the sentence lines that are owned will be used in the parallel corpus (testing). In scenario 2, the test is carried out by comparing the lines of the monolingual corpus sentences after subtracting and adding sentences. In order for scenario 2 to run, accuracy is needed in scenario 1 which is the best. The test was carried out 6 times using BLEU (Bilingual Evaluation Understudy) tools. From the results of the tests carried out, the best accuracy value is 29.83%.

English-Dogri Translation System using MOSES

2016 ◽  
Vol 1 (1) ◽  
pp. 45-49
Author(s):  
Avinash Singh ◽  
Asmeet Kour ◽  
Shubhnandan S. Jamwal
Keyword(s):  
Natural Language Processing ◽  
Machine Translation ◽  
Language Processing ◽  
Statistical Machine Translation ◽  
Translation System ◽  
Parallel Corpus ◽  
English System ◽  
Machine Translation System ◽  
Translation Machine ◽  
Language Pair

The objective behind this paper is to analyze the English-Dogri parallel corpus translation. Machine translation is the translation from one language into another language. Machine translation is the biggest application of the Natural Language Processing (NLP). Moses is statistical machine translation system allow to train translation models for any language pair. We have developed translation system using Statistical based approach which helps in translating English to Dogri and vice versa. The parallel corpus consists of 98,973 sentences. The system gives accuracy of 80% in translating English to Dogri and the system gives accuracy of 87% in translating Dogri to English system.

scholarly journals Phrase Based Statistical Machine Translation Javanese-Indonesian

2021 ◽  
Vol 5 (2) ◽  
pp. 378
Author(s):  
Aufa Eka Putri Lesatari ◽  
Arie Ardiyanti ◽  
Arie Ardiyanti ◽  
Ibnu Asror ◽  
Ibnu Asror
Keyword(s):  
Machine Translation ◽  
Test Data ◽  
Statistical Machine Translation ◽  
Data Sources ◽  
Test Results ◽  
Parallel Corpus ◽  
Translation Machine

This research aims to produce a statistical machine translation that can be implemented to perform Javanese-Indonesian translation and to know the influence of the main data sources of statistical machine translation namely parallel corpus and monolingual corpus on the quality of Javanese-Indonesian statistical machine translation. The testing was carried out by gradually adding the quantity of parallel corpus and monolingual corpus to seven configurations of Javanese-Indonesian statistical machine translation. All machine translation configuration experiments were tested with test data totaling 500 lines of Javanese sentences. Results from machine translation are evaluated automatically using Bilingual Evaluation Understudy (BLEU). Test results in seven configurations showed an increase in the evaluation value of the translation machine after the quantity of parallel corpus and monolingual corpus was added. The quantity of parallel corpus in configurations 1 and 2 increased by 3,6%, configurations 2 and 3 increased by 8,23%, configurations 3 and 7 increased by 14,92%. Additional monolingual corpus quantity in configurations 4 and 5 increased BLEU score by 0,18%, configurations 5 and 6 increased by 0,06%, configurations 6 and 7 increased by 0,24%. The test results showed that the quantity of parallel corpus and monolingual corpus could increase the evaluation value of statistical machine translation Javanese-Indonesian, but the quantity of parallel corpus had a greater influence than the quantity of monolingual corpus

Analyzing Subword Techniques to Improve English to Sinhala Neural Machine Translation

2021 ◽  
pp. 2050017
Author(s):  
Rashmini Naranpanawa ◽  
Ravinga Perera ◽  
Thilakshi Fonseka ◽  
Uthayasanker Thayasivam
Keyword(s):  
Machine Translation ◽  
State Of The Art ◽  
Statistical Machine Translation ◽  
Translation System ◽  
Rare Word ◽  
Neural Machine Translation ◽  
Parallel Corpus ◽  
Low Resource ◽  
Word Level ◽  
Morphologically Rich Languages

Neural machine translation (NMT) is a remarkable approach which performs much better than the Statistical machine translation (SMT) models when there is an abundance of parallel corpus. However, vanilla NMT is primarily based upon word-level with a fixed vocabulary. Therefore, low resource morphologically rich languages such as Sinhala are mostly affected by the out of vocabulary (OOV) and Rare word problems. Recent advancements in subword techniques have opened up opportunities for low resource communities by enabling open vocabulary translation. In this paper, we extend our recently published state-of-the-art EN-SI translation system using the transformer and explore standard subword techniques on top of it to identify which subword approach has a greater effect on English Sinhala language pair. Our models demonstrate that subword segmentation strategies along with the state-of-the-art NMT can perform remarkably when translating English sentences into a rich morphology language regardless of a large parallel corpus.

scholarly journals English-Kazakh Parallel Corpus For Statistical Machine Translation

2014 ◽  
Vol 3 (3) ◽  
pp. 65-72
Author(s):  
Ayana Kuandykova ◽  
Amandyk Kartbayev ◽  
Tannur Kaldybekov
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Parallel Corpus

scholarly journals Improving Machine Translation Performance by Exploiting Non-Parallel Corpora

2005 ◽  
Vol 31 (4) ◽  
pp. 477-504 ◽  
Author(s):  
Dragos Stefan Munteanu ◽  
Daniel Marcu
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Translation System ◽  
Parallel Corpora ◽  
Parallel Corpus ◽  
Scarce Resources ◽  
Parallel Data ◽  
Machine Translation System ◽  
Novel Method ◽  
Arabic And English

We present a novel method for discovering parallel sentences in comparable, non-parallel corpora. We train a maximum entropy classifier that, given a pair of sentences, can reliably determine whether or not they are translations of each other. Using this approach, we extract parallel data from large Chinese, Arabic, and English non-parallel newspaper corpora. We evaluate the quality of the extracted data by showing that it improves the performance of a state-of-the-art statistical machine translation system. We also show that a good-quality MT system can be built from scratch by starting with a very small parallel corpus (100,000 words) and exploiting a large non-parallel corpus. Thus, our method can be applied with great benefit to language pairs for which only scarce resources are available.

The Critical Technology Development Status of Machine Translation

2013 ◽  
Vol 791-793 ◽  
pp. 1622-1625
Author(s):  
Dan Han ◽  
Zhi Han Yu
Keyword(s):  
Machine Translation ◽  
Technology Development ◽  
Statistical Machine Translation ◽  
Word Segmentation ◽  
Translation System ◽  
Chinese Word ◽  
Segmentation Method ◽  
Chinese Word Segmentation ◽  
Critical Technology ◽  
Translation Machine

In this article, we mainly introduce some basic concepts about machine translation. Machine translation means translating a natural language text to another by software. It can be divided into two categories: rule-based and corpus-based. IBM's statistical machine translation, Microsoft's multi-language machine translation project, AT & T's voice translation system and CMUs PANGLOSS system are three typical machine translation systems. Due to sentences are constructed by words continuously in Chinese. Chinese word segmentation is very essential. Three methods of Chinese word segmentation: segmentation methods based on string matching, segmentation method based on the understanding and segmentation method based on the statistics.

Neural machine translation of low-resource languages using SMT phrase pair injection

2020 ◽  
pp. 1-22
Author(s):  
Sukanta Sen ◽  
Mohammed Hasanuzzaman ◽  
Asif Ekbal ◽  
Pushpak Bhattacharyya ◽  
Andy Way
Keyword(s):  
Machine Translation ◽  
Large Scale ◽  
Production Systems ◽  
Statistical Machine Translation ◽  
Training Data ◽  
Original Training ◽  
Neural Machine Translation ◽  
Parallel Corpus ◽  
Low Resource ◽  
Better Than

Abstract Neural machine translation (NMT) has recently shown promising results on publicly available benchmark datasets and is being rapidly adopted in various production systems. However, it requires high-quality large-scale parallel corpus, and it is not always possible to have sufficiently large corpus as it requires time, money, and professionals. Hence, many existing large-scale parallel corpus are limited to the specific languages and domains. In this paper, we propose an effective approach to improve an NMT system in low-resource scenario without using any additional data. Our approach aims at augmenting the original training data by means of parallel phrases extracted from the original training data itself using a statistical machine translation (SMT) system. Our proposed approach is based on the gated recurrent unit (GRU) and transformer networks. We choose the Hindi–English, Hindi–Bengali datasets for Health, Tourism, and Judicial (only for Hindi–English) domains. We train our NMT models for 10 translation directions, each using only 5–23k parallel sentences. Experiments show the improvements in the range of 1.38–15.36 BiLingual Evaluation Understudy points over the baseline systems. Experiments show that transformer models perform better than GRU models in low-resource scenarios. In addition to that, we also find that our proposed method outperforms SMT—which is known to work better than the neural models in low-resource scenarios—for some translation directions. In order to further show the effectiveness of our proposed model, we also employ our approach to another interesting NMT task, for example, old-to-modern English translation, using a tiny parallel corpus of only 2.7K sentences. For this task, we use publicly available old-modern English text which is approximately 1000 years old. Evaluation for this task shows significant improvement over the baseline NMT.

scholarly journals Optimizing Statistical Machine Translation for Text Simplification

2016 ◽  
Vol 4 ◽  
pp. 401-415 ◽  
Author(s):  
Wei Xu ◽  
Courtney Napoles ◽  
Ellie Pavlick ◽  
Quanze Chen ◽  
Chris Callison-Burch
Keyword(s):  
Machine Translation ◽  
Large Scale ◽  
Statistical Machine Translation ◽  
Parallel Corpus ◽  
Iterative Development ◽  
Text Simplification ◽  
Multiple References

Most recent sentence simplification systems use basic machine translation models to learn lexical and syntactic paraphrases from a manually simplified parallel corpus. These methods are limited by the quality and quantity of manually simplified corpora, which are expensive to build. In this paper, we conduct an in-depth adaptation of statistical machine translation to perform text simplification, taking advantage of large-scale paraphrases learned from bilingual texts and a small amount of manual simplifications with multiple references. Our work is the first to design automatic metrics that are effective for tuning and evaluating simplification systems, which will facilitate iterative development for this task.

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