@InProceedings{kachi-akiba-tsukada:2026:wmt,
  author    = {Kachi, Takumi  and  Akiba, Tomoyosi  and  Tsukada, Hajime},
  title     = {AkibaNLP-TUT: Improving Low-Resource Machine Translation via Language-Specific and Length-Adaptive Noise Injection},
  booktitle      = {Proceedings of the Eleventh Conference on Machine Translation},
  month          = {October},
  year           = {2026},
  address        = {Budapest, Hungary},
  publisher      = {Association for Computational Linguistics},
  pages     = {2131--2141},
  abstract  = {We present a language-specific word-level noise injection method for low-resource machine translation that eliminates the need for an external monolingual corpus by constructing the low-resource language vocabulary directly from the source-side text of the target dataset. We also investigate two important hyperparameters of the original method: the maximum edit distance used for candidate word selection and the target noise ratio. Specifically, we propose a dynamic edit-distance constraint based on word length and evaluate multiple noise ratios from 5\% to 30\%. Experiments on Assamese-English translation show that the proposed dynamic constraint consistently outperforms the fixed edit-distance setting across all evaluated noise ratios, while a moderate noise ratio achieves the best translation performance. We further report our official results for the WMT 2026 Low-Resource Indic Language Translation Shared Task and discuss the effectiveness and limitations of the proposed approach under different resource conditions.},
  url       = {https://aclanthology.org/2026.wmt-1.151}
}