@inproceedings{reheman-etal-2025-enhancing,
    title = "Enhancing Neural Machine Translation Through Target Language Data: A $k${NN}-{LM} Approach for Domain Adaptation",
    author = "Reheman, Abudurexiti  and
      Liu, Hongyu  and
      Ruan, Junhao  and
      Abudula, Abudukeyumu  and
      Luo, Yingfeng  and
      Xiao, Tong  and
      Zhu, JingBo",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.496/",
    pages = "10053--10065",
    ISBN = "979-8-89176-251-0",
    abstract = "Neural machine translation (NMT) has advanced significantly, yet challenges remain in adapting to new domains . In scenarios where bilingual data is limited, this issue is further exacerbated. To address this, we propose $k$NN-LM-NMT, a method that leverages semantically similar target language sentences in the $k$NN framework. Our approach generates a probability distribution over these sentences during decoding, and this distribution is then interpolated with the NMT model{'}s distribution. Additionally, we introduce an $n$-gram-based approach to focus on similar fragments, enabling the model to avoid the noise introduced by the non-similar parts. To enhance accuracy, we further incorporate cross-lingual retrieval similarity to refine the $k$NN probability distribution. Extensive experiments on multi-domain datasets demonstrate significant performance improvements in both high-resource and low-resource scenarios. Our approach effectively extracts translation knowledge from limited target domain data, and well benefits from large-scale monolingual data for robust context representation."
}