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.
BibTeX
@inproceedings{tan-etal-2024-narrowing,
title = "Narrowing the Gap between Zero- and Few-shot Machine Translation by Matching Styles",
author = "Tan, Weiting and
Xu, Haoran and
Shen, Lingfeng and
Li, Shuyue Stella and
Murray, Kenton and
Koehn, Philipp and
Van Durme, Benjamin and
Chen, Yunmo",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2024",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-naacl.33/",
doi = "10.18653/v1/2024.findings-naacl.33",
pages = "490--502"
}