论文标题
地区：对话状态跟踪，通过猎犬驱动的封闭式调整
DiSTRICT: Dialogue State Tracking with Retriever Driven In-Context Tuning
论文作者
论文摘要
对话状态跟踪（DST）是面向任务的对话系统的关键组成部分，它通过在正在进行的对话中确定预定插槽的值来表示用户意图。现有的方法使用手工制作的模板和其他插槽信息来微调并促使大型预训练的语言模型，并从对话环境中引起插槽值。需要大量的手动努力和域知识来设计有效的提示，从而限制了这些方法对新领域和任务的普遍性。在这项工作中，我们提出了DISTRIZED DISTRICT，这是DST的一种可推广的内部上下文调整方法，该方法为给定的对话检索了高度相关的培训示例，以无需任何手工制作的模板即可微调模型。使用MultiWoz基准数据集进行的实验表明，使用较小的模型以各种零射击和少量设置的现有方法优于现有方法，从而为通常具有有限资源可用性的现实部署提供了重要的优势。
Dialogue State Tracking (DST), a key component of task-oriented conversation systems, represents user intentions by determining the values of pre-defined slots in an ongoing dialogue. Existing approaches use hand-crafted templates and additional slot information to fine-tune and prompt large pre-trained language models and elicit slot values from the dialogue context. Significant manual effort and domain knowledge is required to design effective prompts, limiting the generalizability of these approaches to new domains and tasks. In this work, we propose DiSTRICT, a generalizable in-context tuning approach for DST that retrieves highly relevant training examples for a given dialogue to fine-tune the model without any hand-crafted templates. Experiments with the MultiWOZ benchmark datasets show that DiSTRICT outperforms existing approaches in various zero-shot and few-shot settings using a much smaller model, thereby providing an important advantage for real-world deployments that often have limited resource availability.
