There has been significant interest in zero and few-shot learning for dialogue state tracking (DST) due to the high cost of collecting and annotating task-oriented dialogues. Recent work has demonstrated that in-context learning requires very little data and zero parameter updates, and even outperforms trained methods in the few-shot setting (Hu et al. 2022). We propose RefPyDST, which advances the state of the art with three advancements to in-context learning for DST. First, we formulate DST as a Python programming task, explicitly modeling language coreference as variable reference in Python. Second, since in-context learning depends highly on the context examples, we propose a method to retrieve a diverse set of relevant examples to improve performance. Finally, we introduce a novel re-weighting method during decoding that takes into account probabilities of competing surface forms, and produces a more accurate dialogue state prediction. We evaluate our approach using MultiWOZ and achieve state-of-the-art multi-domain joint-goal accuracy in zero and few-shot settings.

翻译：随着任务导向型对话数据的收集与注释成本高昂，零样本与少样本学习在对话状态跟踪领域引起了广泛关注。近期研究表明，情境学习仅需极少量数据且无需参数更新，甚至在少样本场景下超越了传统有监督方法（Hu等，2022）。我们提出RefPyDST方法，通过三项创新推动对话状态跟踪情境学习的前沿：首先，将对话状态跟踪形式化为Python编程任务，显式地将语言共指建模为Python变量引用；其次，鉴于情境学习高度依赖上下文示例，我们提出一种检索多样化相关示例的方法以提升性能；最后，引入一种新颖的解码阶段重加权方法，综合考虑竞争表层形式的概率，从而生成更精确的对话状态预测。我们在MultiWOZ数据集上验证该方法，在零样本与少样本场景下均实现了多领域联合目标准确率的最优性能。