@inproceedings{varghese-etal-2024-neural,
title = "Neural Machine Translation for {M}alayalam Paraphrase Generation",
author = "Varghese, Christeena and
Koshelev, Sergey and
Yamshchikov, Ivan",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth and
Nadarajan, Rajeswari and
Ravikiran, Manikandan",
booktitle = "Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
month = mar,
year = "2024",
address = "St. Julian's, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.dravidianlangtech-1.2",
pages = "10--15",
abstract = "This study explores four methods of generating paraphrases in Malayalam, utilizing resources available for English paraphrasing and pre-trained Neural Machine Translation (NMT) models. We evaluate the resulting paraphrases using both automated metrics, such as BLEU, METEOR, and cosine similarity, as well as human annotation. Our findings suggest that automated evaluation measures may not be fully appropriate for Malayalam, as they do not consistently align with human judgment. This discrepancy underscores the need for more nuanced paraphrase evaluation approaches especially for highly agglutinative languages.",
}
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%0 Conference Proceedings
%T Neural Machine Translation for Malayalam Paraphrase Generation
%A Varghese, Christeena
%A Koshelev, Sergey
%A Yamshchikov, Ivan
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%Y Nadarajan, Rajeswari
%Y Ravikiran, Manikandan
%S Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F varghese-etal-2024-neural
%X This study explores four methods of generating paraphrases in Malayalam, utilizing resources available for English paraphrasing and pre-trained Neural Machine Translation (NMT) models. We evaluate the resulting paraphrases using both automated metrics, such as BLEU, METEOR, and cosine similarity, as well as human annotation. Our findings suggest that automated evaluation measures may not be fully appropriate for Malayalam, as they do not consistently align with human judgment. This discrepancy underscores the need for more nuanced paraphrase evaluation approaches especially for highly agglutinative languages.
%U https://aclanthology.org/2024.dravidianlangtech-1.2
%P 10-15
Markdown (Informal)
[Neural Machine Translation for Malayalam Paraphrase Generation](https://aclanthology.org/2024.dravidianlangtech-1.2) (Varghese et al., DravidianLangTech-WS 2024)
ACL
- Christeena Varghese, Sergey Koshelev, and Ivan Yamshchikov. 2024. Neural Machine Translation for Malayalam Paraphrase Generation. In Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 10–15, St. Julian's, Malta. Association for Computational Linguistics.