@inproceedings{maillard-etal-2023-small,
    title = "Small Data, Big Impact: Leveraging Minimal Data for Effective Machine Translation",
    author = "Maillard, Jean  and
      Gao, Cynthia  and
      Kalbassi, Elahe  and
      Sadagopan, Kaushik Ram  and
      Goswami, Vedanuj  and
      Koehn, Philipp  and
      Fan, Angela  and
      Guzman, Francisco",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.154",
    doi = "10.18653/v1/2023.acl-long.154",
    pages = "2740--2756",
    abstract = "For many languages, machine translation progress is hindered by the lack of reliable training data. Models are trained on whatever pre-existing datasets may be available and then augmented with synthetic data, because it is often not economical to pay for the creation of large-scale datasets. But for the case of low-resource languages, would the creation of a few thousand professionally translated sentence pairs give any benefit? In this paper, we show that it does. We describe a broad data collection effort involving around 6k professionally translated sentence pairs for each of 39 low-resource languages, which we make publicly available. We analyse the gains of models trained on this small but high-quality data, showing that it has significant impact even when larger but lower quality pre-existing corpora are used, or when data is augmented with millions of sentences through backtranslation.",
}

For many languages, machine translation progress is hindered by the lack of reliable training data. Models are trained on whatever pre-existing datasets may be available and then augmented with synthetic data, because it is often not economical to pay for the creation of large-scale datasets. But for the case of low-resource languages, would the creation of a few thousand professionally translated sentence pairs give any benefit? In this paper, we show that it does. We describe a broad data collection effort involving around 6k professionally translated sentence pairs for each of 39 low-resource languages, which we make publicly available. We analyse the gains of models trained on this small but high-quality data, showing that it has significant impact even when larger but lower quality pre-existing corpora are used, or when data is augmented with millions of sentences through backtranslation.
Maillard, Jean Gao, Cynthia Kalbassi, Elahe Sadagopan, Kaushik Ram Goswami, Vedanuj Koehn, Philipp Fan, Angela Guzman, Francisco
Small Data, Big Impact: Leveraging Minimal Data for Effective Machine Translation