Being engaging, knowledgeable, and empathetic are all desirable general qualities in a conversational agent. Previous work has introduced tasks and datasets that aim to help agents to learn those qualities in isolation and gauge how well they can express them. But rather than being specialized in one single quality, a good open-domain conversational agent should be able to seamlessly blend them all into one cohesive conversational flow. In this work, we investigate several ways to combine models trained towards isolated capabilities, ranging from simple model aggregation schemes that require minimal additional training, to various forms of multi-task training that encompass several skills at all training stages. We further propose a new dataset, BlendedSkillTalk, to analyze how these capabilities would mesh together in a natural conversation, and compare the performance of different architectures and training schemes. Our experiments show that multi-tasking over several tasks that focus on particular capabilities results in better blended conversation performance compared to models trained on a single skill, and that both unified or two-stage approaches perform well if they are constructed to avoid unwanted bias in skill selection or are fine-tuned on our new task.

本研究探讨了几种结合不同能力的机器学习模型，旨在实现一个综合性的对话系统，并提出了一个新的数据集BlendedSkillTalk，通过比较不同架构和训练方案的表现来分析这些能力如何在自然对话中融合。研究结果表明，相比于单一技能的模型，多任务训练可以更好地提高混合会话的表现，而统一或二阶段方法都可以表现良好，关键在于避免不必要的技能选择偏差或在新任务上进行微调。

评估对话代理程序整合技能的能力