论文标题
通过明确模块化分解的快速，可扩展的对话状态跟踪
Fast and Scalable Dialogue State Tracking with Explicit Modular Decomposition
论文作者
论文摘要
我们提出了一种称为显式模块化分解（EMD）的快速可扩展体系结构，其中我们同时将基于分类的方法和基于提取的方法和设计四个模块（用于分类和序列标记）以共同提取对话状态。基于Multiwoz 2.0数据集的实验结果与最新方法相比，根据复杂性和可伸缩性验证了我们所提出的模型的优越性，尤其是在多域对话的情况下，与许多转弯的话语纠结。
We present a fast and scalable architecture called Explicit Modular Decomposition (EMD), in which we incorporate both classification-based and extraction-based methods and design four modules (for classification and sequence labelling) to jointly extract dialogue states. Experimental results based on the MultiWoz 2.0 dataset validates the superiority of our proposed model in terms of both complexity and scalability when compared to the state-of-the-art methods, especially in the scenario of multi-domain dialogues entangled with many turns of utterances.
