Token repetition is a typical form of multi-modal problem in fully non-autoregressive translation (NAT). In this work, we revisit the multi-modal problem in recently proposed NAT models. Our study reveals that these advanced models have introduced other types of information redundancy errors, which cannot be measured by the conventional metric - the continuous repetition ratio. By manually annotating the NAT outputs, we identify two types of information redundancy errors that correspond well to lexical and reordering multi-modality problems. Since human annotation is time-consuming and labor-intensive, we propose automatic metrics to evaluate the two types of redundant errors. Our metrics allow future studies to evaluate new methods and gain a more comprehensive understanding of their effectiveness.

在这项研究中，我们重新审视了最近提出的全非自回归翻译模型中的多模态问题，并揭示了这些先进模型引入了其他类型的信息冗余错误，无法由传统指标 - 连续重复比率来衡量。通过手动注释非自回归翻译模型的输出，我们确定了与词汇和重新排序的多模式问题相符合的两种信息冗余错误。鉴于人工注释的时间消耗和劳动强度，我们提出了自动度量标准来评估这两种冗余错误类型。我们的度量标准可以让未来的研究评估新方法，并对其有效性有一个更全面的理解。

关于非自回归翻译中的信息冗余