Abstract
Although Machine Translation has historically trusted on huge amounts of parallel corpora, the latest analysis has accomplished to prepare each Neural and Statistical Machine Translation system using monolingual corpora only. In spite of the prospective of this methodology for low-resource settings, obtainable structures square measure way outstanding their supervised counterparts, restraining their concrete interest. In this paper, Sect. 1 contains numerous deficiencies of existing unsupervised SMT approaches by exploiting subword information. Section 2 consists of another methodology established on phrase-based statistical machine translation that significantly cessations the gap with supervised structures. Principled Unsupervised Statistical Machine Translation in Sect. 3. Results and discussions in Sect. 4 and conclusion in Sect. 5.
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Tarakeswara Rao, B., Patibandla, R.S.M.L., Murty, M.R. (2020). A Comparative Study on Effective Approaches for Unsupervised Statistical Machine Translation. In: Bhateja, V., Satapathy, S., Satori, H. (eds) Embedded Systems and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 1076. Springer, Singapore. https://doi.org/10.1007/978-981-15-0947-6_85
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