用领域样本提升文本生成质量，比传统方法更鲁棒。
Case-Based Decision-Theoretic Decoding with Quality Memories
- 基于领域数据样例估计期望效用，改进决策机制。
- 在7个翻译任务和图像描述任务中超越MBR和MAP。
- 适合需要高质量生成的跨领域应用。
最小贝叶斯风险（MBR）解码是一种文本生成的决策规则，通过选择最大化期望效用的假设，生成质量高于最大后验（MAP）解码的文本。然而，它依赖于模型生成的样本，难以捕捉域外知识。为此，我们提出基于案例的决策理论（CBDT）解码，利用领域数据样例估计期望效用。CBDT不仅优于MAP，且在七个域间翻译任务及MSCOCO、nocaps图像描述任务中，结合MBR与CBDT的方案表现超过纯MBR。实验验证其在跨域场景中的有效性。
原文摘要 · Abstract (English)
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.
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