Language localisation of Tamil using Statistical Machine Translation

Achchuthan, Y.; Sarveswaran, Kengatharaiyer

doi:10.1109/icter.2015.7377677

Cited by 1 publication

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine Translation involves the automated translation of text from one language to another by using various algorithms and resources to produce quality translation predictions (Pathak and Pakray, 2018;Krishnamurthy, 2015Krishnamurthy, , 2019. Neural Machine Translation (NMT) brought about a great improvement in the field of MT by overcoming flaws of rule-based and statistical machine translation (SMT) (Revanuru et al, 2017;Achchuthan and Sarveswaran, 2015;Parameswari et al, 2012;Thenmozhi et al, 2018;Kumar et al, 2020b). NMT incorporates the training of neural networks on parallel corpora to predict the likeliness of a sequence of words.…”

Section: Introductionmentioning

confidence: 99%

Attentive fine-tuning of Transformers for Translation of low-resourced languages @LoResMT 2021

Puranik¹,

Hande²,

Priyadharshini³

et al. 2021

Preprint

View full text Add to dashboard Cite

This paper reports the Machine Translation (MT) systems submitted by the IIITT team for the English→Marathi and English⇔Irish language pairs LoResMT 2021 shared task. The task focuses on getting exceptional translations for rather low-resourced languages like Irish and Marathi. We fine-tune IndicTrans, a pretrained multilingual NMT model for English→Marathi, using external parallel corpus as input for additional training. We have used a pretrained Helsinki-NLP Opus MT English⇔Irish model for the latter language pair. Our approaches yield relatively promising results on the BLEU metrics. Under the team name IIITT, our systems ranked 1, 1, and 2 in English→Marathi, Irish→English, and English→Irish respectively. The codes for our systems are published 1 .

show abstract