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.

翻译：自然语言理解包含意图检测（识别用户目标）和槽填充（提取与目标相关的实体）这两项任务。以往的槽填充方法假设每条消息中每种意图类型最多出现一次，然而这一假设在现实场景中往往不成立。本研究通过去除消息中唯一意图的约束条件，对槽填充任务进行了泛化处理。我们将该任务定义为JSON生成问题，并采用语言模型来求解。通过融合DBpedia与现有槽填充数据集，我们构建了适用于JSON生成的预训练数据集；同时利用GPT-3生成了领域内数据集。针对该任务，我们训练了T5模型（包括在提示中添加/去除示例两种设置），结果表明两种训练数据均能提升模型性能，且模型可泛化至训练阶段未出现的意图类型。