Papers by Himanshu Choudhary
Neural Machine Translation for Low-Resourced Indian Languages (2020.lrec-1)
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| Challenge: | Neural machine translation (NMT) is an effective way to convert text to a different language without human involvement. |
| Approach: | They propose to use multihead self-attention along with pre-trained Byte-Pair-Encoded (BPE) and MultiBPE embeddings to develop an efficient machine translation system. |
| Outcome: | The proposed system outperforms Google translator and the existing translators on two of the most morphological rich Indian languages. |
How Low is Too Low? A Computational Perspective on Extremely Low-Resource Languages (2021.acl-srw)
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| Challenge: | Sumerian is one of the world’s oldest written languages attested from at least the beginning of the 3rd millennium BC. |
| Approach: | They propose to use interpretLR to train attention-based deep learning models in a low-resource language, Sumerian cuneiform, which includes part-of-speech tagging, named entity recognition, and machine translation. |
| Outcome: | The proposed pipeline outperforms the large language model RoBERTa for POS Tagging and NER. |