Assamese-English Bilingual Machine Translation

Baruah, Kalyanee Kanchan; Das, Pranjal; Hannan, Md. Abdul; Sarma, Shikhar Kumar

doi:10.5121/ijnlc.2014.3307

Cited by 13 publications

(5 citation statements)

References 3 publications

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“…In recent years, there has been a lot of research and development in the fields of machine translation and transliteration. The encoder-decoder architecture, which serves as the foundation for most NMT algorithms, has demonstrated promising results in various language pairings (Baruah et al 2014). This approach has gained acceptance and implementation in renowned organizations such as Baidu, (Zhou et al 2016), Google (Yonghui et al 2016), andMicrosoft (MSFT 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, the multitude of languages spoken worldwide, exceeding 6500 in number, poses a formidable obstacle to seamless communication (Blasi, Anastasopoulos, and Neubig 2022). In the specific context of Assam, a region located in the northeastern part of India, the predominant language is Assamese, which is extensively used throughout the expansive Brahmaputra Valley (Baruah et al 2014). Tracing its origins back to the seventh century, Assamese language has experienced significant advancements as a written system, which is a branch of the Indo-Aryan language.…”

Section: Introductionmentioning

confidence: 99%

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Improving neural machine translation by integrating transliteration for low-resource English–Assamese language

Nath,

Sarkar,

Mukhopadhyay

et al. 2024

NLP

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In machine translation (MT), one of the challenging tasks is to translate the proper nouns and technical terms from the source language to the target language while preserving the phonetic equivalent of original term. Machine transliteration, an essential part of MT systems, plays a vital role in handling proper nouns and technical terms. In this paper, a hybrid attention-based encoder–decoder machine transliteration system is proposed for the low-resource English to the Assamese language. In this work, the proposed machine transliteration system is integrated with the previously published hybrid attention-based encoder–decoder neural MT model to improve the translation quality of English to the Assamese language. The proposed integrated MT system demonstrated good results across various performance metrics such as BLEU, sacreBLEU, METEOR, chrF, RIBES, and TER for English to Assamese translation. Additionally, human evaluation was also conducted to assess translation quality. The proposed integrated MT system was compared with two existing systems: the Bing translation service model and the Samanantar Indic translation model.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving neural machine translation by integrating transliteration for low-resource English–Assamese language

Nath,

Sarkar,

Mukhopadhyay

et al. 2024

NLP

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show abstract

“…During past a few years, the development of English-Assamese translation system has come into the interest of research community. Using phrase based decoder, translation system for English-Assamese and vice versa has been developed at Gauhati University [3] as well as at Dibrugarh University [19]. Both the systems have been implemented using very small corpus and MOSES as the decoder.…”

Section: Related Studymentioning

confidence: 99%

N-gram based Machine Translation for English-Assamese: Two Languages with High Syntactical Dissimilarity

Hussain,

Borah,

Hannan

2019

IJEAT

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To bridge the language constraint of the people residing in northeastern region of India, machine translation system is a necessity. Large number of people in this region cannot access many services due to the language incomprehensibility. Among several languages spoken, Assamese is one of the major languages used in northeast India. Machine translation for Assamese language is limited compared to other languages. As a result, large number of people using Assamese language cannot avail lots of benefits associated with it. This paper has focused on the development of the English to Assamese translation system using n-gram model. The n-gram model works very well with the language pair having high dissimilarity in syntax compared to other models. The value of n has a very big role in the quality and efficiency of the system. Bilingual Evaluation Understudy (BLEU) score differs significantly with the change of the n-gram. This model uses tuples to reduce the consumption of excess memory and to accelerate the translation process. Parallel corpus has been used for training the n-gram based decoder called MARIE. The number of translation units extracted using n-gram model is much less than the translation units extracted using phrase based model. This has a high impact on system efficiency.

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“…The system claimed BLEU Score of 0.5. P. Das, et al [14] have developed a translation system on corpus-based approach. Language, Decoder, translation and transliteration are the four models that constitute the proposed system.…”

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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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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Assamese-English Bilingual Machine Translation

Abstract: ABSTRACT

Cited by 13 publications

References 3 publications

Improving neural machine translation by integrating transliteration for low-resource English–Assamese language

Improving neural machine translation by integrating transliteration for low-resource English–Assamese language

N-gram based Machine Translation for English-Assamese: Two Languages with High Syntactical Dissimilarity

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

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