Balancing Global Quality and Pronoun-Specific Feedback for Context-Aware Machine Translation

Abstract:Context-aware machine translation can expose the evidence needed for pronoun choice, but standard fine-tuning does not explicitly prioritize these sparse discourse-sensitive decisions. We study ProNMT, a reward-guided iterative self-training method that combines sentence-level quality estimation with a signed confidence signal at generated pronoun positions. For each current sentence and its preceding source context, ProNMT samples candidate translations, scores them using reference-free quality estimation together with a reference-derived pronoun label, and fine-tunes on the highest-scoring candidate. On filtered English--German Europarl and English--French News Commentary data, ProNMT improves over context-aware supervised fine-tuning on BLEU and COMET. Ablations show that pronoun-only feedback can severely degrade sentence-level translation quality on these pronoun-focused data, while hard binary feedback underperforms confidence-weighted feedback. These results indicate that targeted linguistic feedback is most useful when combined with both a global quality signal and the context relevant to the targeted decision. We make the code publicly available at this https URL.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2501.03008 [cs.CL]

(or arXiv:2501.03008v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2501.03008 Focus to learn more arXiv-issued DOI via DataCite

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