We introduce Span-ConveRT, a light-weight model for dialog slot-filling which
frames the task as a turn-based span extraction task. This formulation allows
for a simple integration of conversational knowledge coded in large pretrained
conversational models such as ConveRT (Henderson et al., 2019). We show that
leveraging such knowledge in Span-ConveRT is especially useful for few-shot
learning scenarios: we report consistent gains over 1) a span extractor that
trains representations from scratch in the target domain, and 2) a BERT-based
span extractor. In order to inspire more work on span extraction for the
slot-filling task, we also release RESTAURANTS-8K, a new challenging data set
of 8,198 utterances, compiled from actual conversations in the restaurant
booking domain.

介绍了一个轻量级的模型 Span-ConveRT，用于对话槽填充，任务被视为基于轮的跨度提取。该模型通过与大型预训练对话模型（如 ConveRT）中编码的对话知识的简单集成，允许在少样本学习场景中取得一致的收益。此外，还发布了由真实餐厅预订对话编制的具有挑战性的新数据集 RESTAURANTS-8K，以激励更多的跨度提取对于槽填充任务的研究。