Neural Machine Translation between Myanmar (Burmese) and Dawei (Tavoyan)

Oo, Thazin Myint; Thu, Ye Kyaw; Soe, Khin Mar; Supnithi, Thepchai

doi:10.1109/icca49400.2020.9022813

Cited by 5 publications

(5 citation statements)

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“…Many researchers prefer RNN over basic FFNN in developing machine translation systems because of its state-of-the-art translation. Neural Machine Translation for English to Hindi [20], Attention based English to Punjabi neural machine translation [21], Neural Machine Translation of Indian Languages [16] and Deep Neural Network based Sanskrit to Hindi translation system [17] are some of the recently developed NMT based translation systems. NMT has many advantages over SMT [37][ 38].…”

Section: Neural Machine Translation (Nmt)mentioning

confidence: 99%

“…Authors claimed that better BLEU Score and Word Error Rate have been achieved. T. M. Oo, et al [21] developed a Neural Machine Translation between Myanmar (Burmese) and Dawei (Tavoyan) language. They first developed Myanmar-Dawei parallel corpus which was implemented using two prominent neural machine translation systems, RNN and Transformer with syllable segmentation.…”

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confidence: 99%

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Transcending Linguistic Boundaries: A Technical Exploration of The Evolution and Future Trajectory of Corpus-Based Machine Translation

Vijay Singh Sen

2024

jes

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This review paper presents a comprehensive examination of the evolution, current state, and future perspectives of machine translation (MT). The field of MT, encompassing the automatic translation of text from one language to another, has undergone significant transformations from rule-based methods to statistical models, and more recently, to neural network-based approaches. By synthesizing current research and technological progress, this paper aims to provide a detailed understanding of the dynamic landscape of machine translation and its future directions. In today's globalized world, language barriers remain a significant obstacle to accessing information. Relying solely on human translators often fails to meet the growing demand for translation services, which is why Machine Translation (MT) tools are becoming increasingly popular. These tools have the potential to address this issue effectively. As a result, research in MT is expanding rapidly, and new paradigms are continually being developed. This paper presents a systematic literature review to identify the most widely used MT systems, their architectures, the quality assessment methods used to evaluate them, and which systems deliver the best results.

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Section: Neural Machine Translation (Nmt)mentioning

confidence: 99%

mentioning

confidence: 99%

Transcending Linguistic Boundaries: A Technical Exploration of The Evolution and Future Trajectory of Corpus-Based Machine Translation

Vijay Singh Sen

2024

jes

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“…We developed Burmese-Dawei (9K sentences) [46], Burmese-Beik (10K sentences) [47] and Burmese-Rakhine (18K sentences) [48] parallel corpora. For all language pairs, word segmentation was performed manually.…”

Section: A Corpus Statistics For Baselinementioning

confidence: 99%

Transfer and Triangulation Pivot Translation Approaches for Burmese Dialects

Tanprasert

Thu

et al. 2023

IEEE Access

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Parallel corpora for the languages of Myanmar (Beik, Burmese, Rakhine) are extremely scarce but a necessary requirement for machine translation R&D. Previous studies have proved that pivoting leads to better translation quality if the bridge language is closely related to the source and target language pair. The baseline study is conducted based on the three major approaches of machine translation; Weighted Finite State Transducer (WFST), Phrase-Based Statistical Machine Translation (PBSMT) and Deep Recurrent Neural Network (Deep-RNN). Based on the baseline results, this paper mainly investigated the pivot language technique for PBSMT with Burmese dialects. We employed two different pivot translation methods: transfer (sentence level) and triangulation (phrase level). We present the experimental results on Dawei-Beik, Beik-Dawei translations and Beik-Rakhine, Rakhine-Beik translation via Burmese. Both the transfer and triangulation approaches outperformed the baseline (direct translation), specifically for the Rakhine-Beik language pair. Moreover, the results of the average BiLingual Evaluation Understudy (BLEU), Character ngram F-score (chrF), and Word Error Rate (WER) scores of the 10-fold cross-validation experiments proved that the triangulation pivot has significantly better acceleration than the transfer pivot. We plan to release the parallel corpora of Burmese dialects and present several avenues for further research.INDEX TERMS Burmese dialects, pivot translation, transfer, triangulation, machine translation. '' (''here'') and '' '' (''there'') in the Dawei language. For instance, Dawei word '' '' is same as '' '' in Burmese language and '' '' means '' '' in Burmese language. The question words '' ( ), ( )'' are used in Burmese language; similarly, '' , '' are used instead of '' ( )'' in Dawei language. Moreover, '' '' (''what'') and '' '' (''what happened'') is similar to '' '' and '' '' in Dawei usage. In negative sense of Burmese word '' '' is not used in Dawei word. The negative Dawei words are '' ( )'' or '' '' (''No'' in English). Burmese adverb word '' , , '' (very, extremely) is used as '' , , ''. Some more examples of Dawei vocabularies include '' '', '' '' in Burmese language, (''pregnant'' in English), '' '', '' '' in Burmese language, (''boy'' in English), '' '', '' '' in Burmese language, (''girl'' in English), '' '' '' '' in Burmese language, (''money'' in English), '' '' '' '' in Burmese

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“…VarDial started in 2014, and since then it has become an important venue for work on the study of language variation from a computational perspective, co-located with international NLP conferences such as COLING, EACL, and NAACL. Past editions of the workshop included papers on machine translation (MT) (Shapiro and Duh 2019;Myint Oo, Kyaw Thu, and Mar Soe 2019; C Cambridge University Press 2020 Popović et al 2020), part-of-speech tagging (Huck, Dutka, and Fraser 2019;AlGhamdi and Diab 2019), text normalization (Lusetti et al 2018), and many other relevant topics applied to the computational processing of similar languages, varieties, and dialects. The workshop also featured evaluation campaigns with multiple shared tasks on a number of topics such as crosslingual morphological analysis, cross-lingual parsing, language and dialect identification, and morphosyntactic tagging (Zampieri et al 2018(Zampieri et al , 2019Gȃman et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Natural language processing for similar languages, varieties, and dialects: A survey

Zampieri

Nakov²,

Scherrer

2020

Nat. Lang. Eng.

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There has been a lot of recent interest in the natural language processing (NLP) community in the computational processing of language varieties and dialects, with the aim to improve the performance of applications such as machine translation, speech recognition, and dialogue systems. Here, we attempt to survey this growing field of research, with focus on computational methods for processing similar languages, varieties, and dialects. In particular, we discuss the most important challenges when dealing with diatopic language variation, and we present some of the available datasets, the process of data collection, and the most common data collection strategies used to compile datasets for similar languages, varieties, and dialects. We further present a number of studies on computational methods developed and/or adapted for preprocessing, normalization, part-of-speech tagging, and parsing similar languages, language varieties, and dialects. Finally, we discuss relevant applications such as language and dialect identification and machine translation for closely related languages, language varieties, and dialects.

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Neural Machine Translation between Myanmar (Burmese) and Dawei (Tavoyan)

Cited by 5 publications

References 4 publications

Transcending Linguistic Boundaries: A Technical Exploration of The Evolution and Future Trajectory of Corpus-Based Machine Translation

Transcending Linguistic Boundaries: A Technical Exploration of The Evolution and Future Trajectory of Corpus-Based Machine Translation

Transfer and Triangulation Pivot Translation Approaches for Burmese Dialects

Natural language processing for similar languages, varieties, and dialects: A survey

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