Exposure bias poses a common challenge in numerous natural language processing tasks, particularly in the dialog generation. In response to this issue, researchers have devised various techniques, among which scheduled sampling has proven to be an effective method for mitigating exposure bias. However, the existing state-of-the-art scheduled sampling methods solely consider the current sampling words' quality for threshold truncation sampling, which overlooks the importance of sentence-level information and the method of threshold truncation warrants further discussion. In this paper, we propose a bilevel scheduled sampling model that takes the sentence-level information into account and incorporates it with word-level quality. To enhance sampling diversity and improve the model's adaptability, we propose a smooth function that maps the combined result of sentence-level and word-level information to an appropriate range, and employ probabilistic sampling based on the mapped values instead of threshold truncation. Experiments conducted on the DailyDialog and PersonaChat datasets demonstrate the effectiveness of our proposed methods, which significantly alleviate the exposure bias problem and outperform state-of-the-art scheduled sampling methods.

翻译：暴露偏差是众多自然语言处理任务中常见的挑战，尤其在对话生成领域尤为突出。针对这一问题，研究者们开发了多种技术，其中调度采样已被证明是缓解暴露偏差的有效方法。然而，现有最先进的调度采样方法仅考虑当前采样词的质量进行阈值截断采样，这忽略了句子级信息的重要性，且阈值截断方法仍有待进一步探讨。本文提出了一种双层调度采样模型，该模型同时考虑句子级信息并将其与词级质量相结合。为了提升采样多样性并增强模型的适应性，我们设计了一种平滑函数，将句子级与词级信息的组合结果映射至适当范围，并基于映射后的值采用概率采样替代阈值截断。在DailyDialog和PersonaChat数据集上的实验表明，我们提出的方法显著缓解了暴露偏差问题，且性能优于现有最先进的调度采样方法。