A Review of Machine Translation for South Asian Low Resource Languages

Andrabi, Syed Abdul Basit

doi:10.17762/turcomat.v12i5.1777

Cited by 9 publications

(7 citation statements)

References 25 publications

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“…They have been demonstrated to be crucial in producing excellent results while having a limited operational capability. However, they have trouble creating corpora [10] or the desired language, which can be spoken fluently and with the help of humans. Neural machine translation overcomes the limitations of conventional machine translation [9] methods.…”

Section: Related Workmentioning

confidence: 99%

Language Interpretation Using Machine Learning

Devi¹

2023

IJCIR

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Wouldn't it be cool for your device to translate English language into the language you speak in real life and vice versa? Understanding multiple languages can be a challenging task for humans. In a country like India, where there are over 22 recognized languages, it can be nearly impossible for an individual to learn and comprehend all of them. Consequently, communicating effectively in such a diverse linguistic environment can pose significant obstacles, forcing the need of human interpreters. An interpreter is a platform which translates what is being said in one language into another language without changing its original meaning. Google Translate is one such existing platform but it has drawbacks like not ideal for confidential documents, cannot provide a perfectly accurate translation and requires internet connection to translate. Our goal is to address the limitations mentioned earlier, and to accomplish this objective, we have come up with a project idea that involves real-time translation of spoken languages. The project aims to achieve this by using audio as an input. The proposed system will take real-time English audio as an input, translate it into Telugu and then will produce audio output in Telugu and vice versa. As English is the third most spoken native language in the world after Chinese and Spanish and also the most widely used, we chose to convert the text from English to Telugu and vice versa. Our intention is to utilize a Sequence-to-Sequence model as it is proficient in performing language translations. This model works by transforming a sequence of words from one language into a sequence of different words in another language. It uses neural network for sequence learning.

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Section: Related Workmentioning

confidence: 99%

Language Interpretation Using Machine Learning

Devi¹

2023

IJCIR

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“…An exhaustive review has been conducted on the existing machine translation models that are used in various language conversions and comparisons (5,(13)(14)(15)(16)(17) . A neural machine translation system is developed for the conversion of sentences from Marathi to English.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The Urdu term was mapped to the matching Hindi word using the Hindi-Urdu mapping database. According to industry standards, the system's BLEU score for translating from English to Urdu is 0.3544 (17) .…”

Section: Literature Reviewmentioning

confidence: 99%

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Bi-Lingual Machine Translation Approach using Long Short–Term Memory Model for Asian Languages

Prasanna¹,

Latha²

2023

IJST

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Objectives: To develop an appropriate machine translation model for translating text from English to Tamil. Methods: The proposed work uses a Gated Recurrent Unit (GRU) Long Short-Term Memory (LSTM) model. The Repeat Vector function is used for fitting both the decoder and encoder parts of the network model. Adam optimizer is used because of its faster execution and less consumption of memory. It mainly uses the text corpora which are available in the Internet repository namely Technology Development for Indian Languages (TDIL), Linguistic Data Consortium for Indian Languages (LDCIL), Kaggle, and Ishikahooda. Findings: The motivation for the proposed work emerged from identifying the regional language Tamil as one of the less frequently used languages in the existing translation systems. The Tamil Character Set is one of the challenging factors for the existence of fewer such translation systems. The proposed system produces a BLEU score of 0.9, a Meteor score of 0.98, a TER score of 0.5, a WER score of 20%, an Accuracy rate of 5 (in a 5-point grading scale), and an Adequacy rate of 5 (on a 5-point grading scale) which are significantly better than the existing systems. Novelty: The space complexity of the proposed LSTM-based English Tamil Translator is fine-tuned to 256 units of memory using Adam optimizer for achieving less storage consumption. The number of layers is optimized for reducing the execution time. Unicode Transformation Format (UTF-8) encoding is used to incorporate Tamil language characters. This work has been implemented with a wide range of sentences counted to several thousand. LSTM-based English Tamil Translator is helpful for bilingual learners who are learning specifically Tamil language.

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“…The work by Chopra et al [87] included only one Bangla SMT in their study. The most recent review by Andrabi and Wahid [88] emphasized Hindi and Urdu, and they considered only a few pioneer Bangla studies. Table VII shows the year-wise projection of Bangla-English MT studies with achieved performance scores summarizing the methods presented in Tables II-VI.…”

Section: Significance Of the Present Studymentioning

confidence: 99%

Recent Progress, Emerging Techniques, and Future Research Prospects of Bangla Machine Translation: A Systematic Review

Akhand¹,

Roy²,

Dhar³

et al. 2021

IJACSA

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Machine Translation (MT), the way of translating texts or documents from a source language to a target language automatically without human intervention, has gained popularity in the growing information technology-based era of globalization. Bangla is a major language, and several MT studies with different tools and techniques have been investigated in the last two decades. Considering the importance of the Bangla language and its prospects in MT studies, this study provides a comprehensive review of existing Bangla MT studies to meet the timely demand. Specifically, at first, the basic ideas of different MT methods (Rule-based, Example-based, Statistical, Neural, and Hybrid) and performance measures of MT are presented as a background study of the present review. Then an overview of the Bangla language and a brief description of the available Bangla-English corpora are provided. Next, a description of the existing Bangla MT studies is provided categorically following the common strategic fashion to create a valuable reference for current researchers in the field that is also suitable for nonexpert users. The achieved performances of individual methods are also compared in a tabular form. Finally, a number of future research prospects are revealed from the studies, encouraging researchers and practitioners to develop a better and comprehensive Bangla MT system.

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A Review of Machine Translation for South Asian Low Resource Languages

Cited by 9 publications

References 25 publications

Language Interpretation Using Machine Learning

Language Interpretation Using Machine Learning

Bi-Lingual Machine Translation Approach using Long Short–Term Memory Model for Asian Languages

Recent Progress, Emerging Techniques, and Future Research Prospects of Bangla Machine Translation: A Systematic Review

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