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Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
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 Con-veRT (Henderson et al., 2019a). 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 fromdoi:10.18653/v1/2020.acl-main.11 fatcat:45c22xyu7vhipjsqvo3cunl52m