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- W4286265005 abstract "The language barrier is one of the practical challenges human being face during communication. To overcome this, researchers are focusing on using machines to translate a source language to a target language using the textual representations of the languages. Thus, machine translation (MT) could achieve a near human-level performance in terms of translation quality for several resource-rich languages. However, machine translation performance is still far from a production-level quality for the low resource languages. This work reports a semi-supervised neural machine translation system to boost the translation quality for an extremely resource constraint language pair, i.e. English–Manipuri. Our proposed approach exploits self-training and back-translation in a combined technique. The quantitative evaluation shows that the system performance improves by +0.9 BLEU score after introducing external noise to the input data. Additionally, a multi-reference test dataset developed in-house is used to evaluate the linguistic diversity of the highly agglutinative and morphologically rich Manipuri language. Experimental result attests that the proposed semi-supervised system outperforms the supervised, the pretrained mBART and existing semi-supervised baselines in terms of automatic score and subjective evaluation parameters by a significant margin up to +4.5 and +1.2 BLEU improvements against the supervised and mBART baselines respectively." @default.
- W4286265005 created "2022-07-21" @default.
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- W4286265005 date "2022-12-01" @default.
- W4286265005 modified "2023-10-03" @default.
- W4286265005 title "Low resource machine translation of english–manipuri: A semi-supervised approach" @default.
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- W4286265005 doi "https://doi.org/10.1016/j.eswa.2022.118187" @default.
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