@inproceedings{nishida-etal-2024-generating,
    title = "Generating Diverse Translation with Perturbed $k${NN}-{MT}",
    author = "Nishida, Yuto  and
      Morishita, Makoto  and
      Kamigaito, Hidetaka  and
      Watanabe, Taro",
    editor = "Falk, Neele  and
      Papi, Sara  and
      Zhang, Mike",
    booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = mar,
    year = "2024",
    address = "St. Julian{'}s, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.eacl-srw.2",
    pages = "9--31",
    abstract = "Generating multiple translation candidates would enable users to choose the one that satisfies their needs.Although there has been work on diversified generation, there exists room for improving the diversity mainly because the previous methods do not address the overcorrection problem{---}the model underestimates a prediction that is largely different from the training data, even if that prediction is likely.This paper proposes methods that generate more diverse translations by introducing perturbed $k$-nearest neighbor machine translation ($k$NN-MT).Our methods expand the search space of $k$NN-MT and help incorporate diverse words into candidates by addressing the overcorrection problem.Our experiments show that the proposed methods drastically improve candidate diversity and control the degree of diversity by tuning the perturbation{'}s magnitude.",
}