Narrowing the Gap between Zero- and Few-shot Machine Translation by Matching Styles

Weiting Tan, Haoran Xu, Lingfeng Shen, Shuyue Stella Li, Kenton Murray, Philipp Koehn, Benjamin Van Durme, Yunmo Chen


Abstract
Large language models trained primarily in a monolingual setting have demonstrated their ability to generalize to machine translation using zero- and few-shot examples with in-context learning. However, even though zero-shot translations are relatively good, there remains a discernible gap comparing their performance with the few-shot setting. In this paper, we investigate the factors contributing to this gap and find that this gap can largely be closed (for about 70%) by matching the writing styles of the target corpus. Additionally, we explore potential approaches to enhance zero-shot baselines without the need for parallel demonstration examples, providing valuable insights into how these methods contribute to improving translation metrics.
Anthology ID:
2024.findings-naacl.33
Volume:
Findings of the Association for Computational Linguistics: NAACL 2024
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
490–502
Language:
URL:
https://aclanthology.org/2024.findings-naacl.33
DOI:
Bibkey:
Cite (ACL):
Weiting Tan, Haoran Xu, Lingfeng Shen, Shuyue Stella Li, Kenton Murray, Philipp Koehn, Benjamin Van Durme, and Yunmo Chen. 2024. Narrowing the Gap between Zero- and Few-shot Machine Translation by Matching Styles. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 490–502, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Narrowing the Gap between Zero- and Few-shot Machine Translation by Matching Styles (Tan et al., Findings 2024)
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PDF:
https://aclanthology.org/2024.findings-naacl.33.pdf
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 2024.findings-naacl.33.copyright.pdf