Despite their widespread adoption, neural conversation models have yet to exhibit natural chat capabilities with humans. In this research, we examine user utterances as causes and generated responses as effects, recognizing that changes in a cause should produce a different effect. To further explore this concept, we have compiled and expanded upon a new dataset called CausalDialogue through crowd-sourcing. This dataset includes multiple cause-effect pairs within a directed acyclic graph (DAG) structure. Our analysis reveals that traditional loss functions struggle to effectively incorporate the DAG structure, leading us to propose a causality-enhanced method called Exponential Maximum Average Treatment Effect (ExMATE) to enhance the impact of causality at the utterance level in training neural conversation models. To evaluate the needs of considering causality in dialogue generation, we built a comprehensive benchmark on CausalDialogue dataset using different models, inference, and training methods. Through experiments, we find that a causality-inspired loss like ExMATE can improve the diversity and agility of conventional loss function and there is still room for improvement to reach human-level quality on this new dataset.

翻译：尽管神经对话模型已被广泛采用，但它们尚未展现出与人类进行自然对话的能力。在本研究中，我们将用户话语视为因，生成的响应视为果，并认识到因的变化应产生不同的果。为进一步探索这一概念，我们通过众包方式整理并扩展了一个名为CausalDialogue的新数据集，该数据集包含有向无环图（DAG）结构内的多组因果对。我们的分析表明，传统损失函数难以有效整合DAG结构，因此我们提出了一种名为指数最大平均处理效应（ExMATE）的因果增强方法，以在训练神经对话模型时增强话语级因果关系的影响。为了评估对话生成中考虑因果关系的需求，我们利用不同模型、推理及训练方法，在CausalDialogue数据集上构建了全面的基准测试。通过实验，我们发现类似ExMATE的因果启发损失函数能够提升传统损失函数的多样性与灵活性，且在此新数据集上，要达到人类水平的对话质量仍有改进空间。