Neural language models often fail to generate diverse and informative texts, limiting their applicability in real-world problems. While previous approaches have proposed to address these issues by identifying and penalizing undesirable behaviors (e.g., repetition, overuse of frequent words) from language models, we propose an alternative approach based on an observation: models primarily learn attributes within examples that are likely to cause degeneration problems. Based on this observation, we propose a new approach to prevent degeneration problems by training two models. Specifically, we first train a model that is designed to amplify undesirable patterns. We then enhance the diversity of the second model by focusing on patterns that the first model fails to learn. Extensive experiments on two tasks, namely language modeling and dialogue generation, demonstrate the effectiveness of our approach.

翻译：神经语言模型通常难以生成多样化和信息丰富的文本，限制了其在现实问题中的应用。以往方法通过识别并惩罚语言模型中的不良行为（例如重复、过度使用高频词）来解决这些问题，而我们则基于一个观察提出了替代方法：模型主要从示例中学习那些可能导致退化问题的属性。基于这一观察，我们提出了一种新方法，通过训练两个模型来防止退化问题。具体而言，我们首先训练一个旨在放大不良模式的模型，然后通过聚焦于第一个模型未能学习的模式，增强第二个模型的多样性。在语言建模和对话生成两项任务上的大量实验证明了我们方法的有效性。