论文标题
多域对话行为和响应共同创造
Multi-Domain Dialogue Acts and Response Co-Generation
论文作者
论文摘要
对于以任务为导向的对话系统，产生流利和信息性的响应至关重要。现有的管道方法通常首先预测多个对话行为，并使用它们来协助响应生成。这种方法至少有两个缺点。首先，忽略了多域对话行为的固有结构。其次，行为和响应之间的语义关联并未考虑到响应的产生。为了解决这些问题，我们提出了一个神经共同生成模型，该模型同时产生对话行为和反应。与这些管道方法不同，我们的ACT生成模块保留了多域对话行为的语义结构，而我们的响应生成模块则根据需要动态处理不同的行为。我们使用不确定性损失共同训练这两个模块，以适应其任务权重。大规模的多沃兹数据集进行了广泛的实验，结果表明，在自动和人类评估中，我们的模型比几个最新模型都具有非常有利的改进。
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.
