Dialogue state tracking (DST) is a component of the task-oriented dialogue system. It is responsible for extracting and managing slot values according to dialogue utterances, where each slot represents an essential part of the information to accomplish a task, and slot value is updated recurrently in each dialogue turn. However, many DST models cannot update slot values appropriately. These models may repeatedly inherit wrong slot values extracted in previous turns, resulting in the fail of the entire DST task. They cannot update indirectly mentioned slots well, either. This study designed a model with a mentioned slot pool (MSP) to tackle the update problem. The MSP is a slot-specific memory that records all mentioned slot values that may be inherited, and our model updates slot values according to the MSP and the dialogue context. Our model rejects inheriting the previous slot value when it predicates the value is wrong. Then, it re-extracts the slot value from the current dialogue context. As the contextual information accumulates with the dialogue progress, the new value is more likely to be correct. It also can track the indirectly mentioned slot by picking a value from the MSP. Experimental results showed our model reached state-of-the-art DST performance on MultiWOZ 2.1 and 2.2 datasets.
翻译:对话状态跟踪( DST) 是任务导向对话系统的一个组成部分 。 它负责根据对话语句提取和管理空档值, 每个空档代表了完成某项任务所需的信息的一个基本部分, 时档值在每次对话中反复更新 。 但是, 许多 DST 模式无法适当地更新空档值 。 这些模式可能会反复继承先前轮转中提取的错误空档值, 导致整个 DST 任务失败 。 它们也无法间接更新整个 DST 任务 。 本研究设计了一个模型, 并有提及空档库( MSP) 来处理更新问题 。 MSP 是一个特定空档的记忆, 记录了所有提及空档值, 并且根据MSP 和对话框的背景, 和我们的模式更新空档值值值值 。 我们的模型在假设值错误时拒绝继承以前的空档值 。 然后, 重新从当前对话中提取空档值 。 随着对话进展累积的背景资料, 新值更有可能被更正 。 它还可以通过从 MSP 和 DWO 上选择一个值来跟踪间接提到的空档值 。 。 实验结果显示我们模型已经达到的状态 2. 。