@inproceedings{deguchi-nagata-2025-case,
    title = "Case-Based Decision-Theoretic Decoding with Quality Memories",
    author = "Deguchi, Hiroyuki  and
      Nagata, Masaaki",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.emnlp-main.1710/",
    doi = "10.18653/v1/2025.emnlp-main.1710",
    pages = "33679--33694",
    ISBN = "979-8-89176-332-6",
    abstract = "Minimum Bayes risk (MBR) decoding is a decision rule of text generation, which selects the hypothesis that maximizes the expected utility and robustly generates higher-quality texts than maximum a posteriori (MAP) decoding.However, it depends on sample texts drawn from the text generation model; thus, it is difficult to find a hypothesis that correctly captures the knowledge or information of out-of-domain.To tackle this issue, we propose case-based decision-theoretic (CBDT) decoding, another method to estimate the expected utility using examples of domain data.CBDT decoding not only generates higher-quality texts than MAP decoding, but also the combination of MBR and CBDT decoding outperformed MBR decoding in seven domain De{--}En and Ja$\leftrightarrow$En translation tasks and image captioning tasks on MSCOCO and nocaps datasets."
}