English–Mizo Machine Translation using neural and statistical approaches

Pathak, Amarnath; Bentham, Jereemi

doi:10.1007/s00521-018-3601-3

Cited by 43 publications

(10 citation statements)

References 27 publications

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“…[7] conducted a study to evaluate the English to Mizo NMT system on several test datasets from different domains. [26] conducted a study on English to Mizo MT systems (SMT and NMT) on a training dataset collected from various online sources. [27] extended the work of [26] with additional training dataset of 31,764 parallel sentences and evaluated their systems on three tests dataset of sizes 100, 100, and 798 sentences.…”

Section: Related Workmentioning

confidence: 99%

An empirical study on English-Mizo Statistical Machine Translation with Bible Corpus

Devi

Meetei

Purkayastha

2022

Int. j. electr. comput. eng. syst. (Online)

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Machine Translation (MT) is the process of automatically converting the text or speech in one natural language to another language with the help of a machine. This work presents a Bidirectional Statistical Machine Translation (SMT) system of an extremely low resource language pair Mizo-English, built in a low resource setting. A total of 30800 sentences are collected from the English Bible dataset and manually translated to Mizo by a native linguistic expert to generate the English-Mizo parallel dataset. After subjecting to various pre-processing steps, the parallel dataset is used to build our MT system using MOSES tools. Our framework uses different tools, such as GIZA++ for creating the Translation Model (TM) and IRSTLM to determine the probability of the target model. The quality of our MT system is evaluated using two automatic evaluation metrics: BLEU and METEOR. Our MT systems are also manually evaluated using two parameters: adequacy and fluency.

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Section: Related Workmentioning

confidence: 99%

An empirical study on English-Mizo Statistical Machine Translation with Bible Corpus

Devi

Meetei

Purkayastha

2022

Int. j. electr. comput. eng. syst. (Online)

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“…The efforts of Mizo language under the NLP is a new task which recently gained the interest among researchers. The paper [1] has compared the NMT and PBSMT, and the the result showed that the NMT performed better on the ground of BLEU but fluency though the prediction had less performance compared to PBSMT. Moreover, their analysis results have shown that the NMT has more potential to cope with the long sentence comparing with the PBSMT.…”

Section: Related Workmentioning

confidence: 99%

“…We have investigated the ambiguity, various Prefixes and Suffixes of Mizo language. The English and Mizo languages are different in words order, English Language follows Subject + Verb + Object while the Mizo Language follows 'Object + Subject+ Verb' [16], but sometimes it does follow 'Subject + Verb + Object' same as English [1]. Beyond this, in English the second person pronoun "you" is used for expressing both the singular and plural whereas the Mizo language does have specific words for this (for singular "i" and plural "in").…”

Section: Challenges Of Mt On Mizo Languagementioning

confidence: 99%

“…Mizo Language is a language family of Sino-Tibetan that has 830,846 native speakers as per 2011 census which is mainly spoke in the north-eastern part of India such as Mizoram, Chin State of Burma and in the Chittagong Hill tracts of Bangladesh [1]. In Mizo society there is an increasing interest and need for learning English as there is an increasing demand day by day since most of the information sources are available in the form of English language and Machine Translation is one of the approaches to meet this challenge.…”

Section: Introductionmentioning

confidence: 99%

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Machine Translation of English Language to Mizo Language

Thihlum

Khenglawt

Debnath

2020

2020 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM)

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The communication between different regional languages is always inevitable fact which requires a lot of effort to make meaningful in terms of technology. The machine translation methods have been endeavoured to fill this impediment. The Neural Machine Translation is one of the efforts which recently gained a remarkable enhancement in the matter of human judgement over the conventional approaches like phrasebased machine translation and statistical machine translation. Thus, the advances of neural machine translation approach influence the challenge in the mind of people. The different types of online translation and mobile application tools such as Google Translate, DeepL, SYSTRAN, etc. applications were came up to tackle this issue. But such kinds of convenience translation tools are not available for English-Mizo parallel languages. In this paper, we have tried to enhance the ability of language translation by owing the supremacy capability of Neural Machine Translation to improve the English to Mizo conversion in BiLingual Evaluation Understudy metric.

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“…As a specialized English, business English contains a rich culture, including business traditions and customs in different countries and business etiquette habits [1]. For the business English translation, national cultural images are often lost or distorted, which may cause people to misunderstand other cultures and lead to transaction failures.…”

Section: Introductionmentioning

confidence: 99%

Research on Business English Translation Framework Based on Speech Recognition and Wireless Communication

Wu²

2021

Mobile Information Systems

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In order to improve the accuracy of English translation, reduce the error rate of translation results, and increase the correction rate of translation, this paper proposes a business English translation architecture design based on speech recognition and wireless communication. The architecture is partitioned according to the functions of the overall system design, and the voice acquisition module, voice processing module, and peripheral circuit module are designed according to functional requirements. Among them, speech recognition helps users to perform language translation to reduce the possibility of errors in the translation process. At the same time, it uses wireless communication technology to construct a business English translation corpus to meet the personal needs of users. The paper also uses an improved translation model for translation error correction and intelligent proofreading, which improves the reliability of translation results. Experimental results show that the system has a high error correction rate and error correction rate, and the translation results have a certain degree of reliability, which fully verifies the effectiveness and application value of the system.

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English–Mizo Machine Translation using neural and statistical approaches

Cited by 43 publications

References 27 publications

An empirical study on English-Mizo Statistical Machine Translation with Bible Corpus

An empirical study on English-Mizo Statistical Machine Translation with Bible Corpus

Machine Translation of English Language to Mizo Language

Research on Business English Translation Framework Based on Speech Recognition and Wireless Communication

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