One of the most important challenges in text generation systems is to produce
outputs that are not only correct but also diverse. Recently, Minimum
Bayes-Risk (MBR) decoding has gained prominence for generating sentences of the
highest quality among the decoding algorithms. However, existing algorithms
proposed for generating diverse outputs are predominantly based on beam search
or random sampling, thus their output quality is capped by these underlying
methods. In this paper, we investigate an alternative approach -- we develop
diversity-promoting decoding algorithms by enforcing diversity objectives to
MBR decoding. We propose two variants of MBR, Diverse MBR (DMBR) and
$k$-medoids MBR (KMBR), methods to generate a set of sentences with high
quality and diversity. We evaluate DMBR and KMBR on a variety of directed text
generation tasks using encoder-decoder models and a large language model with
prompting. The experimental results show that the proposed method achieves a
better trade-off than the diverse beam search and sampling algorithms.

生成文本系统中的关键挑战之一是产生不仅正确而且多样化的输出。本文提出了两种多样化的最小贝叶斯风险解码算法（DMBR 和 KMBR），通过在贝叶斯风险解码中引入多样性目标，生成高质量和多样化的句子集合。实验结果表明，所提出的方法在多个文本生成任务中比现有的多样化算法更好地平衡了质量和多样性。