AISFG: Abundant Information Slot Filling Generator
Yan T. Yang, Junda Ye, Zhongbao Zhang, Liwen Wang · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · 2022
As an essential component of task-oriented dialogue systems, slot filling requires enormous labeled training data in a certain domain.However, in most cases, there is little or no target domain training data is available in the training stage.Thus, cross-domain slot filling has to cope with the data scarcity problem by zero/few-shot learning.Previous researches on zero/few-shot cross-domain slot filling focus on slot descriptions and examples while ignoring the slot type ambiguity and example ambiguity issues.To address these problems, we propose Abundant Information Slot Filling Generator (AISFG), a generative model with a novel query template that incorporates domain descriptions, slot descriptions, and examples with context.Experimental results show that our model outperforms state-of-the-art approaches in zero/few-shot slot filling task. 1