@InProceedings{sheng-dimarco-fraser:2026:wmt,
  author    = {Sheng, Qimin  and  Di Marco, Marion  and  Fraser, Alexander},
  title     = {In-Context Learning for Upper Sorbian to German Translation: Comparing Lexical and Morpho-Syntactic Information},
  booktitle      = {Proceedings of the Eleventh Conference on Machine Translation},
  month          = {October},
  year           = {2026},
  address        = {Budapest, Hungary},
  publisher      = {Association for Computational Linguistics},
  pages     = {705--717},
  abstract  = {LLM-based translation remains challenging for low-resource languages, as LLMs often lack linguistic knowledge due to their limited representation in pre-training data. This is particularly problematic for morphologically rich languages where a large vocabulary can lead to further data sparsity. Previous work has shown that providing external guidance on the source sentence in the form of language-specific information can help LLMs with tasks such as machine translation. In this study, we investigate zero-shot context augmentation for Upper Sorbian-to-German translation by enriching prompts with different types of contextual information: translation candidates in the form of bilingual dictionary entries, and morpho-syntactic analysis provided through morphological tagging. We first compare the effectiveness of these types of information in a zero-shot setting and then evaluate whether they generalize across additional experimental settings. We find that context-augmented prompting can improve Upper Sorbian-to-German translation. Contexts only containing translation candidates perform overall best, whereas morpho-syntactic analysis offers limited benefits, both on its own and in combination with lexical cues.},
  url       = {https://aclanthology.org/2026.wmt-1.39}
}