This repository contains the code for our paper: In-context Examples Selection for Machine Translation.

Abstract: Large-scale generative models show an impressive ability to perform a wide range of Natural Language Processing (NLP) tasks using in-context learning, where a few examples are used to describe a task to the model. For Machine Translation (MT), these examples are typically randomly sampled from the development dataset with a similar distribution as the evaluation set. However, it is unclear how the choice of these in-context examples and their ordering impacts the output translation quality. In this work, we aim to understand the properties of good in-context examples for MT in both in-domain and out-of-domain settings. We show that the translation quality and the domain of the in-context examples matter and that 1-shot noisy unrelated example can have a catastrophic impact on output quality. While concatenating multiple random examples reduces the effect of noise, a single good prompt optimized to maximize translation quality on the development dataset can elicit learned information from the pre-trained language model. Adding similar examples based on an n-gram overlap with the test source significantly and consistently improves the translation quality of the outputs, outperforming a strong kNN-MT baseline in 2 out of 4 out-of-domain datasets.

Note: The experiments reported in the paper were run using internal fairseq code, so the numbers might not exactly match in the paper but the overall trends should be the same.

The multi-domain German-English dataset can be obtained from here. Unzip using unzip multi_domain_new_split.zip -d multi-domain.

Scripts to train the models reported in Table 2 and 3 can be found in run_exps.sh.

If you make use of the code, models, or algorithm, please cite our paper:

@article{agrawal2022context,
  title={In-context Examples Selection for Machine Translation},
  author={Agrawal, Sweta and Zhou, Chunting and Lewis, Mike and Zettlemoyer, Luke and Ghazvininejad, Marjan},
  journal={arXiv preprint arXiv:2212.02437},
  year={2022}
}