Dialogue state tracking models play an important role in a task-oriented dialogue system. However, most of them model the slot types conditionally independently given the input. We discover that it may cause the model to be confused by slot types that share the same data type. To mitigate this issue, we propose TripPy-MRF and TripPy-LSTM that models the slots jointly. Our results show that they are able to alleviate the confusion mentioned above, and they push the state-of-the-art on dataset MultiWoZ 2.1 from 58.7 to 61.3. Our implementation is available at https://github.com/CTinRay/Trippy-Joint.

翻译：对话状态跟踪模式在面向任务的对话系统中发挥着重要作用,但是,大多数模式根据输入的情况,以有条件的方式独立地模拟空档类型。我们发现,这可能导致该模式被共享相同数据类型的空档类型所混淆。为缓解这一问题,我们提议TripPy-MRF和TripPy-LSTM共同模拟空档。我们的结果表明,它们能够缓解上述混乱,并将关于多WoZ 2.1数据集的最新技术从58.7提高到61.3。 我们的落实情况可在https://github.com/CTinRay/Trippy-United网站上查阅。