Sequence-to-sequence state-of-the-art systems for dialogue state tracking (DST) use the full dialogue history as input, represent the current state as a list with all the slots, and generate the entire state from scratch at each dialogue turn. This approach is inefficient, especially when the number of slots is large and the conversation is long. We propose Diable, a new task formalisation that simplifies the design and implementation of efficient DST systems and allows one to easily plug and play large language models. We represent the dialogue state as a table and formalise DST as a table manipulation task. At each turn, the system updates the previous state by generating table operations based on the dialogue context. Extensive experimentation on the MultiWoz datasets demonstrates that Diable (i) outperforms strong efficient DST baselines, (ii) is 2.4x more time efficient than current state-of-the-art methods while retaining competitive Joint Goal Accuracy, and (iii) is robust to noisy data annotations due to the table operations approach.

翻译：当前基于序列到序列的对话状态追踪（DST）系统将完整对话历史作为输入，以列表形式表示所有槽位的当前状态，并在每轮对话中从头生成完整状态。这种方法效率低下，尤其在槽位数量多、对话篇幅长的情况下尤为突出。我们提出Diable，一种新的任务规范化形式，简化了高效DST系统的设计与实现，并支持大型语言模型的即插即用。我们将对话状态表示为表格，并将DST规范化为表操作任务。在每轮对话中，系统通过基于对话上下文生成表操作来更新先前状态。在MultiWoz数据集上的大量实验表明，Diable（i）性能优于强基线高效DST模型，（ii）在保持竞争性联合目标准确率的同时，时间效率比当前最优方法提升2.4倍，（iii）其基于表操作的方法对噪声数据标注具有鲁棒性。