Generating fluent and informative responses is of critical importance for task-oriented dialogue systems. Existing pipeline approaches generally predict multiple dialogue acts first and use them to assist response generation. There are at least two shortcomings with such approaches. First, the inherent structures of multi-domain dialogue acts are neglected. Second, the semantic associations between acts and responses are not taken into account for response generation. To address these issues, we propose a neural co-generation model that generates dialogue acts and responses concurrently. Unlike those pipeline approaches, our act generation module preserves the semantic structures of multi-domain dialogue acts and our response generation module dynamically attends to different acts as needed. We train the two modules jointly using an uncertainty loss to adjust their task weights adaptively. Extensive experiments are conducted on the large-scale MultiWOZ dataset and the results show that our model achieves very favorable improvement over several state-of-the-art models in both automatic and human evaluations.

我们提出了一个神经共同生成模型，可以同时生成对话行为和响应，相比于现有的流水线方法，我们的模型可以保留多领域对话行为的语义结构并动态关注不同的对话行为，通过在 MultiWOZ 数据集上的测试，我们的模型在自动评估和人类评估中都比几种最先进的模型表现得要好很多。

多领域对话行为与响应同时生成