Knowledge-aware audio-grounded generative slot filling for limited annotated data

Guangzhi Sun, Chao Zhang, Ivan Vulic, Pawel Budzianowski, Philip C. Woodland

Abstract: Highlights•A knowledge-aware audio-grounded (KA2G) generative slot-filling framework is proposed•KA2G integrates knowledge with two tree-constrained pointer generator (TCPGen)•4.6% and 11.2% SLU-F1 increases achieved for rare and unseen entities respectively.•KA2G achieved 20% joint goal accuracy (JGA) improvements on multi-turn dialogue.•The importance of the two TCPGen components were verified via comprehensive analyses