A repetition is a response that repeats words in the previous speaker's utterance in a dialogue. Repetitions are essential in communication to build trust with others, as investigated in linguistic studies. In this work, we focus on repetition generation. To the best of our knowledge, this is the first neural approach to address repetition generation. We propose Weighted Label Smoothing, a smoothing method for explicitly learning which words to repeat during fine-tuning, and a repetition scoring method that can output more appropriate repetitions during decoding. We conducted automatic and human evaluations involving applying these methods to the pre-trained language model T5 for generating repetitions. The experimental results indicate that our methods outperformed baselines in both evaluations.

翻译：重复是重复前一位发言者在对话中发言时所说的话的一种回应。在语言研究中,重复对于与他人建立信任的沟通至关重要。在这项工作中,我们注重重复的生成。根据我们的知识,这是解决重复的生成的第一个神经方法。我们建议了加权标签平滑,一种在微调中明确学习用词重复的平滑方法,以及一种重复的评分方法,在解码过程中可以产生更适当的重复。我们进行了自动和人文评估,将这种方法应用到预先培训过的T5语言模型中来产生重复。实验结果表明,我们的方法在两次评估中都超过了基准。