论文标题
对话图：非确定对话管理的数据扩展，培训和评估
Conversation Graph: Data Augmentation, Training and Evaluation for Non-Deterministic Dialogue Management
论文作者
论文摘要
面向任务的对话系统通常依赖大量的高质量培训数据或需要复杂的手工规则。但是，考虑到对话的复杂性，现有数据集通常限制在大小上。此外，常规训练信号推断不适用于非确定性代理行为，即在相同的对话状态下将多个动作视为有效。我们提出了对话图（Convgraph），这是一种基于图的对话表示，可以利用该对话图，以用于数据增强，多次参考训练和评估非确定性代理。 Convgraph生成了新颖的对话路径，以增加数据量和多样性。跨三个数据集的内在和外在评估表明，使用Convgraph的数据增强和/或多参考培训可以提高对话成功率高达6.4％。
Task-oriented dialogue systems typically rely on large amounts of high-quality training data or require complex handcrafted rules. However, existing datasets are often limited in size considering the complexity of the dialogues. Additionally, conventional training signal inference is not suitable for non-deterministic agent behaviour, i.e. considering multiple actions as valid in identical dialogue states. We propose the Conversation Graph (ConvGraph), a graph-based representation of dialogues that can be exploited for data augmentation, multi-reference training and evaluation of non-deterministic agents. ConvGraph generates novel dialogue paths to augment data volume and diversity. Intrinsic and extrinsic evaluation across three datasets shows that data augmentation and/or multi-reference training with ConvGraph can improve dialogue success rates by up to 6.4%.
