Papers by Vikrant Goyal
LTRC-MT Simple & Effective Hindi-English Neural Machine Translation Systems at WAT 2019 (D19-52)
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| Challenge: | Neural Machine Translation (NMT) is a promising approach for low resource languages. |
| Approach: | They propose to use both Recurrent Neural Networks & Transformer architectures to train NMT models. |
| Outcome: | The proposed model outperforms Statistical Machine Translation (SMT) techniques on a low resource Hindi-English language pair. |
Efficient Neural Machine Translation for Low-Resource Languages via Exploiting Related Languages (2020.acl-srw)
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| Challenge: | Neural Machine Translation (NMT) is a rapidly advancing MT paradigm that can be used to improve machine translation for many languages. |
| Approach: | They propose a technique called Unified Transliteration and Subword Segmentation to leverage language similarity while exploiting parallel data from related languages. |
| Outcome: | The proposed approach improves translation accuracy by 5 BLEU points over the standard Transformer-based NMT models. |
Linguistically Informed Hindi-English Neural Machine Translation (2020.lrec-1)
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| Challenge: | Neural Machine Translation (NMT) is a promising approach to machine translation . lack of parallel training data for Hindi-English is limiting . |
| Approach: | They propose to incorporate linguistic knowledge encoded by Hindi phenomena into a Transformer model to improve the translation performance. |
| Outcome: | The proposed model incorporates linguistic features to improve the translation performance. |