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：当前最先进的序列到序列对话状态跟踪（DST）系统以完整对话历史为输入，将当前状态表示为包含所有槽位的列表，并在每一轮对话中从头生成完整状态。这种方法效率低下，尤其在槽位数量众多且对话冗长时尤为显著。我们提出Diable这一新型任务形式化框架，它简化了高效DST系统的设计与实现，并支持轻松集成各种大语言模型。我们将对话状态表示为表格，并将DST形式化为表格操作任务。在每一轮对话中，系统通过基于对话上下文生成表格操作来更新先前状态。在MultiWoz数据集上的大量实验表明，Diable：（i）优于强效的基座DST模型；（ii）在保持竞争性联合目标准确率的同时，时间效率较当前最先进方法提升2.4倍；（iii）由于采用表操作方法，对噪声数据标注具有鲁棒性。