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- W4288391476 abstract "Machine translation (MT) is automatically converting a piece of text from one language to another. The work provides a comparison and analysis of the phrase-based statistical machine translation (PBSMT) system using monolingual corpora that interpret English text to Hindi. MT quality has improved dramatically over the previous two decades, making it appealing for usage in the translation industry. The importance of MT cannot be overstated. The application of MT systems has been bounded due to the dependency on the bilingual corpus for a range of language pairings. The current paper presents a comparison of two possible cross-lingual word embedding mapping approaches utilizing monolingual datasets for the SMT methodology. When translating from a language to some other language without supervision, inter-lingual word embedding is crucial. We have implemented and compared two cross-lingual mapping approaches are employed in this paper: adversarial training and self-learning methods. The experimental findings for several evaluation methodologies, such as BLEU, METEOR, METEOR-Hi, TER, WER, MER, and NIST, show that the PBSMT approach delivers superior translation using the self-learning method than the adversarial training method for English–Hindi." @default.
- W4288391476 created "2022-07-29" @default.
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- W4288391476 date "2022-01-01" @default.
- W4288391476 modified "2023-09-30" @default.
- W4288391476 title "Analysis of Unsupervised Statistical Machine Translation Using Cross-Lingual Word Embedding for English–Hindi" @default.
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- W4288391476 doi "https://doi.org/10.1007/978-981-19-0745-6_7" @default.
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