Machine translation vs transliteration8/28/2023 ![]() Pp 315–321īollacker K, Cook R, Tufts P (1962–1963) (2007) freebase: A shared database of structured general human knowledge. In: Coling 2004: Proceedings of the 20th international conference on computational linguistics. pp 186–189īlatz J, Fitzgerald E, Foster G et al (2004) Confidence estimation for machine translation. In: proceedings of the third linguistic annotation workshop (LAW III). Comput Linguistics 11:111–121īhatt R, Narasimhan B, Palmer M et al (2009) A multi-representational and multi-layered treebank for hindi/urdu. 1-34īennett WS, Slocum J (1985) The LRC machine translation system. Calcutta, India, pp 1–9īawa S, Kumar M (2021) A comprehensive survey on machine translation for English, Hindi and Sanskrit languages. In: proceedings of the 7th state Science and technology congress,(SSTC’00). arXiv preprint arXiv:14090473īandyopadhyay S (2000) ANUBAAD-the translator from English to Indian languages. Central Institute of Indian Languages,, accessed March 2022Īpps B2017 T English to Maithili Dictionary,, accessed December 2021Īssociation ELR ELRA-ELDA Portal,, accessed March 2022Īttardi G Github - attardi/wikiextractor,, accessed March 2022īahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. Comput Intell Neurosci 2022:1–11Īnnamalai E, Indian Languages CI of (1979) Language movements in India. ![]() pp 4–6Īndrabi SAB, Wahid A (2022) Machine translation system using deep learning for English to Urdu. In: proceedings of 5th international conference on natural language processing (ICON 2007). Proceedings of the Third Workshop on Statistical Machine Translation, In, pp 115–118Īmbati V, Rohini U (2007) A hybrid approach to example based machine translation for Indian languages. 1-1Īgarwal A, Lavie A (2008) Meteor, m-bleu and m-ter: evaluation metrics for high-correlation with human rankings of machine translation output. 01, 2022).Īchanta SD, Karthikeyan T, Kanna RV (2021) Wearable sensor based acoustic gait analysis using phase transition-based optimization algorithm on IoT. ![]() pp 265–283.Ībout Us | AI4Bharat IndicNLP. In: 12th symposium on operating systems design and implementation. The article also provides a direction in which further research for Indian languages should ideally be headed.Ībadi M, Barham P, Chen J et al (2016) Tensorflow: A system for large-scale machine learning. This article determines the current status of available datasets, MT and MTn systems, along with commenting on the validity of currently available evaluation metrics like BLEU for Indian languages. ![]() The review also discusses the scope for future research in this field. It explores different approaches like statistics oriented, example oriented, and neural network-oriented MT techniques implied in MT tasks, along with providing insight into the work carried out so far for Indian languages. The lack of readily available grammatical rules, the distinction between proper and common nouns, and large datasets, along with additional linguistic complexity compared to many other languages, makes developing such systems for Indian languages even more complicated. MT and MTn systems are an evolving field of computational linguistics and are considered to be incredibly challenging to develop. It also comments on the validity and viability of various evaluation metrics for Indian languages. This paper is unique in providing a detailed review of all steps involved in the NLP system development pipeline, from the creation and collection of data to the development of the system, and furthermore, the evaluation and analysis of the system. This paper presents a review of Natural Language Processing (NLP) techniques like Machine Translation (MT) and Machine Transliteration (MTn), along with providing an analytical study of evaluation metrics such as BLEU (BiLingual Evaluation Understudy) score and discussing datasets available for MT and MTn systems in Indian languages. In today’s global scenario, frequent international and domestic interactions necessitate the application of Machine Transliteration and Translation systems to overcome the language barrier. ![]()
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