论文标题
改善有限标记的对话状态跟踪，并进行自我设计
Improving Limited Labeled Dialogue State Tracking with Self-Supervision
论文作者
论文摘要
现有的对话状态跟踪（DST）模型需要大量标记的数据。但是，收集高质量的标签是昂贵的，尤其是当域数量增加时。在本文中，我们解决了一个很少讨论的实用DST问题，即使用有限的标记数据有效地学习。我们提出并研究了两个自制的目标：保留潜在的一致性和建模对话行为。我们鼓励DST模型具有一致的潜在分布，并且给定一个扰动的输入，使其对看不见的情况更加健壮。我们还添加了辅助语音生成任务，建模对话行为与对话状态之间的潜在相关性。实验结果表明，当在Multiwoz数据集中使用1 \％标记的数据时，我们提出的自我监督信号可以提高关节目标准确性8.95 \％。如果某些未标记的数据被共同培训为半监督学习，我们可以取得额外的1.76 \％改进。我们分析和可视化我们提出的自制信号如何帮助DST任务，并希望刺激未来的数据效率DST研究。
Existing dialogue state tracking (DST) models require plenty of labeled data. However, collecting high-quality labels is costly, especially when the number of domains increases. In this paper, we address a practical DST problem that is rarely discussed, i.e., learning efficiently with limited labeled data. We present and investigate two self-supervised objectives: preserving latent consistency and modeling conversational behavior. We encourage a DST model to have consistent latent distributions given a perturbed input, making it more robust to an unseen scenario. We also add an auxiliary utterance generation task, modeling a potential correlation between conversational behavior and dialogue states. The experimental results show that our proposed self-supervised signals can improve joint goal accuracy by 8.95\% when only 1\% labeled data is used on the MultiWOZ dataset. We can achieve an additional 1.76\% improvement if some unlabeled data is jointly trained as semi-supervised learning. We analyze and visualize how our proposed self-supervised signals help the DST task and hope to stimulate future data-efficient DST research.
