Translate Smart, not Hard: Cascaded Translation Systems with Quality-Aware Deferral

Larger models often outperform smaller ones but come with high computational costs. Cascading offers a potential solution. By default, it uses smaller models and defers only some instances to larger, more powerful models. However, designing effective deferral rules remains a challenge. In this paper, we propose a simple yet effective approach for machine translation, using existing quality estimation (QE) metrics as deferral rules. We show that QE-based deferral allows a cascaded system to match the performance of a larger model while invoking it for a small fraction (30% to 50%) of the examples, significantly reducing computational costs. We validate this approach through both automatic and human evaluation.

@article{farinhas2025_2502.12701,
  title={ Translate Smart, not Hard: Cascaded Translation Systems with Quality-Aware Deferral },
  author={ António Farinhas and Nuno M. Guerreiro and Sweta Agrawal and Ricardo Rei and André F.T. Martins },
  journal={arXiv preprint arXiv:2502.12701},
  year={ 2025 }
}