(Eds.), Proceedings of TIAD-2019 Shared Task - Translation Inference Across Dictionaries co-located with the 2nd Language, Data and Knowledge Conference (LDK 2019),, Leipzig, Germany, 2019, pp. Arcan M., Torregrosa D., Ahmadi S., McCrae J.P., Inferring translation candidates for multilingual dictionary generation with multi-way neural machine translation, in: Gracia J., Kabashi B., Kernerman I.Akella K., Allu S.H., Suresh Ragupathi S., Singhal A., Khan Z., Jawahar C., P., Namboodiri V., Exploring pair-wise NMT for Indian languages, in: Proceedings of the 17th International Conference on Natural Language Processing (ICON), NLP Association of India (NLPAI), Indian Institute of Technology Patna, Patna, India, 2020, pp.Aji A.F., Bogoychev N., Heafeld K., Sennrich R., In neural machine translation, what does transfer learning transfer?, in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, 2020, pp.(Eds.), Information and Communication Technology and Applications, Springer International Publishing, Cham, 2021, pp. Abdulmumin I., Galadanci B.S., Isa A., Enhanced back-translation for low resource neural machine translation using self-training, in: Misra S., Muhammad-Bello B.Towards a Cleaner Document-Oriented Multilingual Crawled Corpus. Abadji, J., Ortiz Suarez, P., Romary, L., Sagot, B., 2022.There are a few language-specific survey papers on ILNMT, and this is one of the first kinds of survey papers where all the information is gathered under one canopy. In this survey paper, ILNMT architectures for different Indic languages are covered, e.g., Hindi, Tamil (HRLs), Kannada, Marathi (LRLs), Sinhala, and Nepali (ZRLs). The vision behind this literature survey paper is to make this paper a collective source for all information regarding the predominant ILNMT architectures, the toolkits available for building NMT models, and various pre-trained language models needed by researchers who contribute to the ILNMT research community. Many Indic languages are classified into HRLs, LRLs, and ZRLs based on corpus availability. Based on the corpus availability, the languages are categorized into High Resource Languages (HRLs), Low Resource Languages (LRLs), and Zero Resource Languages (ZRLs). Hence, there is increasing demand in the research to address the challenges of developing applicable MT models even when minuscule training data is available. Automated machine translation models are unavailable for some less spoken Indic languages like Kashmiri and Dogri. Though NMT for Indic languages (ILNMT) is giving better results for majority speaking language pairs, the translation quality is low due to a lack of significant resources. Numerous NMT architectures are floating across the international and national research pool many claims to be state-of-the-art architectures. Slowly, NMT paved its path into Indian MT research and witnessed many works for various language pairs in this regard. With the invention of deep learning concepts, Machine Translation (MT) migrated towards Neural Machine Translation (NMT) architectures, eventually from Statistical Machine Translation (SMT), which ruled MT for a few decades.
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