@InProceedings{saud-dawadi-regmi:2026:wmt,
  author    = {Saud, Shiv Ram  and  Dawadi, Sundeep  and  Regmi, Sunil},
  title     = {paramanandaAI@WMT26 Video Subtitle Translation: QLoRA Fine-Tuning with Test-Time Metadata Prompting},
  booktitle      = {Proceedings of the Eleventh Conference on Machine Translation},
  month          = {October},
  year           = {2026},
  address        = {Budapest, Hungary},
  publisher      = {Association for Computational Linguistics},
  pages     = {2615--2621},
  abstract  = {Subtitle translation poses challenges dis tinct from general-purpose machine trans lation model trained on web corpus. The outputs must fit strict display constraints as the text is predominantly spoken dia logue, local slangs, proper names, cultural nuances. In the "WMT26 Chinese → En glish task" all test videos belong to the historical costume drama genre, where pe riod vocabulary cannot be resolved from a single line in isolation. We address these challenges with a two-part system. We fine-tune Hy-MT2-1.8B (Zheng et al., 2026) on 500,000 Chinese-English subtitle pairs from the TVsub corpus (Wang et al., 2018) using 4-bit QLoRA (Dettmers et al., 2023), and at test time we inject show-level metadata (title, episode name, summary) into a structured system prompt to ground character names and period terms. On an in-domain 200-pair development split, QLoRA fine-tuning raises BLEU from 6.93 to 31.29 (+24.4 points) which demon strates the value of domain adaptation over the base multilingual model. Across four prompt configurations evaluated via LLM-as-judge on 20 sampled test trans lations the metadata-augmented, previous context-free variant (structured\_noctx) wins 14 of 20 judged dialogues and is sent for submission. The fine-tuned model weights are released under Apache 2.0 at ShivRamSaud/hy-mt2-1.8b-wmt26.},
  url       = {https://aclanthology.org/2026.wmt-1.204}
}