Dialogue state tracking (DST) is an important step in dialogue management to keep track of users' beliefs. Existing works fine-tune all language model (LM) parameters to tackle the DST task, which requires significant data and computing resources for training and hosting. The cost grows exponentially in the real-world deployment where dozens of fine-tuned LM are used for different domains and tasks. To reduce parameter size and better utilize cross-task shared information, we propose to use soft prompt token embeddings to learn task properties. Without tuning LM parameters, our method drastically reduces the number of parameters needed to less than 0.5% of prior works while achieves better low-resource DST performance.

翻译：对话状态跟踪（DST）是对话管理中的关键步骤，用于追踪用户信念。现有方法通过微调所有语言模型（LM）参数来处理DST任务，这需要大量数据和计算资源进行训练与部署。在实际应用中，当数十个微调后的LM被用于不同领域和任务时，成本呈指数级增长。为减少参数规模并更好地利用跨任务共享信息，我们提出使用软提示令牌嵌入来学习任务特性。无需调整LM参数，我们的方法将所需参数数量大幅减少至先前工作的0.5%以下，同时实现了更优的低资源DST性能。