@InProceedings{mash-EtAl:2026:wmt,
  author    = {Mash, Audrey  and  Ayebakuro, Jonathan Orama  and  Bohman, Ella Paulina  and  Liao, Xixian  and  De Luca Fornaciari, Francesca  and  Melero, Maite},
  title     = {BSC Submission for WMT26 General Machine Translation Shared Task},
  booktitle      = {Proceedings of the Eleventh Conference on Machine Translation},
  month          = {October},
  year           = {2026},
  address        = {Budapest, Hungary},
  publisher      = {Association for Computational Linguistics},
  pages     = {1334--1350},
  abstract  = {We present the BSC submission to the WMT26 General Machine Translation shared task: a 7B decoder-only model covering 23 directions, adapted from the SALAMANDRA (Gonzalez-Agirre et al., 2025) family through vocabulary replacement, two rounds of continued pre-training (monolingual, followed by instruction-formatted parallel data), supervised fine-tuning on a translation-only instruction mixture, and preference optimisation applied selectively to six weak directions via CPO-SimPO (Xu et al., 2024; Meng et al., 2024) with LoRA adapters (Hu et al., 2021). Submissions are decoded with Minimum Bayes Risk selection over a candidate pool filtered for truncation, degeneracy and off-target script. As official human evaluation was unavailable at the time of writing, we report a stage-wise internal evaluation on FLORES+ and WMT24++, together with automatic scores for the submitted system on the 12 test-set directions for which references have been released. Instruction tuning accounts for almost all measurable gain, but its size differs by an order of magnitude between the two evaluation sets; preference optimisation moves automatic metrics by less than the drift among untreated directions. The model is released under a Research-Only Licence and is available on request.},
  url       = {https://aclanthology.org/2026.wmt-1.72}
}