@InProceedings{rao-EtAl:2026:wmt,
  author    = {Rao, Soujanya  and  Taneja, Sakhil  and  Chaitanya, Krishna  and  Mamidi, Radhika},
  title     = {Decepticons-IIITH : Morphology-Aware Lexicon Filtering of Back-Translated Data for Low-Resource English-to-Indic Translation},
  booktitle      = {Proceedings of the Eleventh Conference on Machine Translation},
  month          = {October},
  year           = {2026},
  address        = {Budapest, Hungary},
  publisher      = {Association for Computational Linguistics},
  pages     = {2214--2219},
  abstract  = {This study tackles the WMT 2026 Low-Resource Indic Language Translation task, covering English → Assamese, Bodo, Mizo and Nagamese. Low-resource languages lack parallel corpora of sufficient size and quality to train machine translation models. To address this challenge, we propose Morphology-Aware Lexicon Filtering of Back-Translated data to generate good-quality parallel corpora. Initially, monolingual text from publicly available sources is back-translated to English with an NLLB-200-3.3B model fine-tuned on the official training data. To filter this synthetic data, we introduce MALM (Morphology-Aware Lexicon Matching), a filter that checks whether words from a bilingual lexicon are preserved in a translation, using Morfessor stems to handle rich morphology and edit-distance matching to handle borrowed words. For our primary method, training occurs in two stages: fine-tuning on filtered synthetic (back-translated) data followed by fine-tuning on the official parallel data. Whereas for the contrastive method, we are only fine-tuning on the official parallel data. We fine-tune the NLLB-200-3.3B model for Assamese, Mizo, Nagamese and the IndicTrans2-1B model for Bodo. Our primary systems ranked first among primary submissions on two Category-2 pairs viz. English → Bodo and English → Nagamese. On Nagamese, where only 2,000 official sentence pairs exist, of the two methods we used, the primary system improved by a BLEU score of 8.33.},
  url       = {https://aclanthology.org/2026.wmt-1.160}
}