Natural language understanding includes the tasks of intent detection
(identifying a user's objectives) and slot filling (extracting the entities
relevant to those objectives). Prior slot filling methods assume that each
intent type cannot occur more than once within a message, however this is often
not a valid assumption for real-world settings. In this work, we generalize
slot filling by removing the constraint of unique intents in a message. We cast
this as a JSON generation task and approach it using a language model. We
create a pre-training dataset by combining DBpedia and existing slot filling
datasets that we convert for JSON generation. We also generate an in-domain
dataset using GPT-3. We train T5 models for this task (with and without
exemplars in the prompt) and find that both training datasets improve
performance, and that the model is able to generalize to intent types not seen
during training.

本文提出了一种更普适的槽位填充方法，通过将其视为 JSON 生成任务，并使用语言模型进行处理。本文结合 DBpedia 和现有的槽位填充数据集以及 GPT-3 生成属于该领域的数据集，使用 T5 模型进行训练，并发现加入 prompt 后两个训练集的性能都有所提高。最终模型可以泛化到训练期间未见过的意图类型。