Video-guided machine translation paper lifts low-resource Indic languages by +1.75 BLEU
kalyan_kpl · x · 2026-11-09
- An AACL/IJCNLP 2026 long paper explores video-guided machine translation (VMT): leveraging video context to disambiguate ambiguous words and surface attributes like object color or a person's gender during translation.
- Prior VMT work trains models from scratch on large datasets, mostly for English–Chinese. This paper studies strategies that work with limited data, extending VMT to Indic languages: Hindi, Marathi, and Odia.
- The authors propose a synthetic data framework suited to low-resource VMT training.
- Results show +1.75 BLEU improvement over baselines.
Paper: ACL Anthology
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