arXiv AI September 15, 2026 By Harshit Dhankhar, Baban Gain, Asif Ekbal, Yogesh Mani Tripathi

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

The paper introduces ProNMT, a reward-guided iterative self‑training approach that balances global translation quality with pronoun‑specific feedback for context‑aware machine translation. ProNMT samples candidate translations, scores them using reference‑free quality estimation and a pronoun label derived from references, and fine‑tunes on the highest‑scoring candidate. Experiments on English–German Europarl and English–French News Commentary show that ProNMT outperforms standard context‑aware fine‑tuning on BLEU and COMET, while ablations reveal that pronoun‑only feedback can harm overall quality and that confidence‑weighted feedback outperforms hard binary feedback.

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