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arXiv · 2502.12701

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

António Farinhas·Nuno M. Guerreiro·Sweta Agrawal·Ricardo Rei·André F. T. Martins
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Abstract

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.

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António Farinhas, Nuno M. Guerreiro, Sweta Agrawal, Ricardo Rei, André F. T. Martins. 2025-02-18. Translate Smart, not Hard: Cascaded Translation Systems with Quality-Aware Deferral. https://arxiv.org/abs/2502.12701

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