@inproceedings{yousofi-bhattacharyya-2024-reconsidering,
title = "Reconsidering {SMT} Over {NMT} for Closely Related Languages: A Case Study of {P}ersian-{H}indi Pair",
author = "Yousofi, Waisullah and
Bhattacharyya, Pushpak",
editor = "Lalitha Devi, Sobha and
Arora, Karunesh",
booktitle = "Proceedings of the 21st International Conference on Natural Language Processing (ICON)",
month = dec,
year = "2024",
address = "AU-KBC Research Centre, Chennai, India",
publisher = "NLP Association of India (NLPAI)",
url = "https://aclanthology.org/2024.icon-1.17/",
pages = "149--156",
abstract = "This paper demonstrates that Phrase-Based Statistical Machine Translation (PBSMT) can outperform Transformer-based Neural Machine Translation (NMT) in moderate-resource scenarios, specifically for structurally similar languages, Persian-Hindi pair in our case. Despite the Transformer architecture`s typical preference for large parallel corpora, our results show that PBSMT achieves a BLEU score of 66.32, significantly exceeding the Transformer-NMT score of 53.7 ingesting the same dataset."
}
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<abstract>This paper demonstrates that Phrase-Based Statistical Machine Translation (PBSMT) can outperform Transformer-based Neural Machine Translation (NMT) in moderate-resource scenarios, specifically for structurally similar languages, Persian-Hindi pair in our case. Despite the Transformer architecture‘s typical preference for large parallel corpora, our results show that PBSMT achieves a BLEU score of 66.32, significantly exceeding the Transformer-NMT score of 53.7 ingesting the same dataset.</abstract>
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%0 Conference Proceedings
%T Reconsidering SMT Over NMT for Closely Related Languages: A Case Study of Persian-Hindi Pair
%A Yousofi, Waisullah
%A Bhattacharyya, Pushpak
%Y Lalitha Devi, Sobha
%Y Arora, Karunesh
%S Proceedings of the 21st International Conference on Natural Language Processing (ICON)
%D 2024
%8 December
%I NLP Association of India (NLPAI)
%C AU-KBC Research Centre, Chennai, India
%F yousofi-bhattacharyya-2024-reconsidering
%X This paper demonstrates that Phrase-Based Statistical Machine Translation (PBSMT) can outperform Transformer-based Neural Machine Translation (NMT) in moderate-resource scenarios, specifically for structurally similar languages, Persian-Hindi pair in our case. Despite the Transformer architecture‘s typical preference for large parallel corpora, our results show that PBSMT achieves a BLEU score of 66.32, significantly exceeding the Transformer-NMT score of 53.7 ingesting the same dataset.
%U https://aclanthology.org/2024.icon-1.17/
%P 149-156
Markdown (Informal)
[Reconsidering SMT Over NMT for Closely Related Languages: A Case Study of Persian-Hindi Pair](https://aclanthology.org/2024.icon-1.17/) (Yousofi & Bhattacharyya, ICON 2024)
ACL