论文标题
使用描述性规则零射击多域对话框状态跟踪
Zero-shot Multi-Domain Dialog State Tracking Using Descriptive Rules
论文作者
论文摘要
在这项工作中，我们提出了一个将描述性逻辑规则纳入最先进的神经网络中的框架，使他们能够学习如何在不引入任何新培训数据的情况下处理看不见的标签。该规则通过网络损失函数的附加术语集成到现有网络中，而无需修改其体系结构，该术语惩罚了不遵守设计规则的网络状态。作为研究案例，该框架应用于现有的基于神经的对话状态跟踪器。我们的实验表明，逻辑规则的包含允许预测看不见的标签，而不会降低原始系统的预测能力。
In this work, we present a framework for incorporating descriptive logical rules in state-of-the-art neural networks, enabling them to learn how to handle unseen labels without the introduction of any new training data. The rules are integrated into existing networks without modifying their architecture, through an additional term in the network's loss function that penalizes states of the network that do not obey the designed rules. As a case of study, the framework is applied to an existing neural-based Dialog State Tracker. Our experiments demonstrate that the inclusion of logical rules allows the prediction of unseen labels, without deteriorating the predictive capacity of the original system.
