Neural Machine Translation for Malayalam Paraphrase Generation

Christeena Varghese, Sergey Koshelev, Ivan P. Yamshchikov


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.
Anthology ID:
2024.dravidianlangtech-1.2
Volume:
Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
Month:
March
Year:
2024
Address:
St. Julian's, Malta
Editors:
Bharathi Raja Chakravarthi, Ruba Priyadharshini, Anand Kumar Madasamy, Sajeetha Thavareesan, Elizabeth Sherly, Rajeswari Nadarajan, Manikandan Ravikiran
Venues:
DravidianLangTech | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10–15
Language:
URL:
https://aclanthology.org/2024.dravidianlangtech-1.2
DOI:
Bibkey:
Cite (ACL):
Christeena Varghese, Sergey Koshelev, and Ivan P. 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.
Cite (Informal):
Neural Machine Translation for Malayalam Paraphrase Generation (Varghese et al., DravidianLangTech-WS 2024)
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PDF:
https://aclanthology.org/2024.dravidianlangtech-1.2.pdf
Video:
 https://aclanthology.org/2024.dravidianlangtech-1.2.mp4