Open-domain dialogue systems aim to interact with humans through natural language texts in an open-ended fashion. Despite the recent success of super large dialogue systems such as ChatGPT, using medium-to-small-sized dialogue systems remains the common practice as they are more lightweight and accessible; however, generating diverse dialogue responses is challenging, especially with smaller models. In this work, we propose an Equal-size Hard Expectation--Maximization (EqHard-EM) algorithm to train a multi-decoder model for diverse dialogue generation. Our algorithm assigns a sample to a decoder in a hard manner and additionally imposes an equal-assignment constraint to ensure that all decoders are well-trained. We provide detailed theoretical analysis to justify our approach. Further, experiments on two large-scale open-domain dialogue datasets verify that our EqHard-EM algorithm generates high-quality diverse responses.

翻译：开放域对话系统旨在通过自然语言文本以开放式的方式与人类进行交互。尽管近年来诸如ChatGPT等超大型对话系统取得了成功，但使用中小型对话系统仍是一种普遍做法，因为它们更为轻量且易于获取；然而，生成多样化的对话响应极具挑战性，尤其是在使用较小模型的情况下。在本工作中，我们提出一种等规模硬期望最大化（EqHard-EM）算法，用于训练多解码器模型以实现多样化对话生成。我们的算法以硬分配方式将样本分配给某一解码器，并额外施加等分配约束以确保所有解码器均得到充分训练。我们提供了详细的理论分析以论证该方法的合理性。此外，在两个大规模开放域对话数据集上的实验验证了我们的EqHard-EM算法能够生成高质量且多样化的响应。