@inproceedings{jinnai-2025-document,
title = "Document-Level Text Generation with Minimum {B}ayes Risk Decoding using Optimal Transport",
author = "Jinnai, Yuu",
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://aclanthology.org/2025.acl-long.1370/",
doi = "10.18653/v1/2025.acl-long.1370",
pages = "28260--28279",
ISBN = "979-8-89176-251-0",
abstract = "Document-level text generation tasks are known to be more difficult than sentence-level text generation tasks as they require an understanding of longer context to generate high-quality texts. In this paper, we investigate the adaptation of Minimum Bayes Risk (MBR) decoding for document-level text generation tasks. MBR decoding makes use of a utility function to estimate the output with the highest expected utility from a set of candidate outputs. Although MBR decoding is shown to be effective in a wide range of sentence-level text generation tasks, its performance on document-level text generation tasks is limited, as many of the utility functions are designed for evaluating the utility of sentences. To this end, we propose MBR-OT, a variant of MBR decoding using Wasserstein distance to compute the utility of a document using a sentence-level utility function. The experimental result shows that the performance of MBR-OT outperforms that of the standard MBR in document-level machine translation, text simplification, and dense image captioning tasks."
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%0 Conference Proceedings
%T Document-Level Text Generation with Minimum Bayes Risk Decoding using Optimal Transport
%A Jinnai, Yuu
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F jinnai-2025-document
%X Document-level text generation tasks are known to be more difficult than sentence-level text generation tasks as they require an understanding of longer context to generate high-quality texts. In this paper, we investigate the adaptation of Minimum Bayes Risk (MBR) decoding for document-level text generation tasks. MBR decoding makes use of a utility function to estimate the output with the highest expected utility from a set of candidate outputs. Although MBR decoding is shown to be effective in a wide range of sentence-level text generation tasks, its performance on document-level text generation tasks is limited, as many of the utility functions are designed for evaluating the utility of sentences. To this end, we propose MBR-OT, a variant of MBR decoding using Wasserstein distance to compute the utility of a document using a sentence-level utility function. The experimental result shows that the performance of MBR-OT outperforms that of the standard MBR in document-level machine translation, text simplification, and dense image captioning tasks.
%R 10.18653/v1/2025.acl-long.1370
%U https://aclanthology.org/2025.acl-long.1370/
%U https://doi.org/10.18653/v1/2025.acl-long.1370
%P 28260-28279
Markdown (Informal)
[Document-Level Text Generation with Minimum Bayes Risk Decoding using Optimal Transport](https://aclanthology.org/2025.acl-long.1370/) (Jinnai, ACL 2025)
ACL