Decoupling Representation and Knowledge for Few-Shot Intent Classification and Slot Filling
DOI:
https://doi.org/10.1609/aaai.v38i16.29775Keywords:
NLP: Conversational AI/Dialog Systems, NLP: Text ClassificationAbstract
Few-shot intent classification and slot filling are important but challenging tasks due to the scarcity of finely labeled data. Therefore, current works first train a model on source domains with sufficiently labeled data, and then transfer the model to target domains where only rarely labeled data is available. However, experience transferring as a whole usually suffers from gaps that exist among source domains and target domains. For instance, transferring domain-specific-knowledge-related experience is difficult. To tackle this problem, we propose a new method that explicitly decouples the transferring of general-semantic-representation-related experience and the domain-specific-knowledge-related experience. Specifically, for domain-specific-knowledge-related experience, we design two modules to capture intent-slot relation and slot-slot relation respectively. Extensive experiments on Snips and FewJoint datasets show that our method achieves state-of-the-art performance. The method improves the joint accuracy metric from 27.72% to 42.20% in the 1-shot setting, and from 46.54% to 60.79% in the 5-shot setting.Downloads
Published
2024-03-24
How to Cite
Han, J., Zou, Y., Wang, H., Wang, J., Liu, W., Wu, Y., Zhang, T., & Li, R. (2024). Decoupling Representation and Knowledge for Few-Shot Intent Classification and Slot Filling. Proceedings of the AAAI Conference on Artificial Intelligence, 38(16), 18171-18179. https://doi.org/10.1609/aaai.v38i16.29775
Issue
Section
AAAI Technical Track on Natural Language Processing I