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. In this paper, 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：序列到序列的对话状态追踪最新系统将完整对话历史作为输入，将当前状态表示为包含所有槽位的列表，并在每轮对话中从头生成完整状态。这种方法效率低下，尤其在槽位数量众多且对话较长时。本文提出新任务形式化方法Diable，该方案简化了高效对话状态追踪系统的设计与实现，并允许研究者便捷地接入大型语言模型。我们将对话状态表示为表格，并将对话状态追踪形式化为表格操作任务：系统基于对话上下文生成表格操作，从而在每轮更新前一状态。在MultiWoz数据集上的大量实验表明，Diable (i) 显著优于强效对话状态追踪基线模型，(ii) 在保持竞争性联合目标准确率的同时，比当前最新方法提升2.4倍时间效率，(iii) 由于采用表格操作方案，对噪声标注数据具有鲁棒性。