Decoupling Representation and Knowledge for Few-Shot Intent Classification and Slot Filling
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.
Code (0)
등록된 구현이 없습니다.
Tasks
intent-classificationIntent ClassificationIntent Classification and Slot FillingRelationslot-fillingSlot FillingSimilar Papers 제목 키워드 기반
Instance Relation Learning Network with Label Knowledge Propagation for Few-shot Multi-label Intent Detection
Few-shot Multi-label Intent Detection (MID) is crucial for dialogue systems, aiming to detect multiple intents of utterances in low-resource dialogue domains. Previous studies focus on a two-stage pipeline. They first le…
Intent DetectionRepresentation based meta-learning for few-shot spoken intent recognition
Spoken intent detection has become a popular approach to interface with various smart devices with ease. However, such systems are limited to the preset list of intents-terms or commands, which restricts the quick custom…
Classificationintent-classificationIntent ClassificationIntent Detection+2Generalized zero-shot audio-to-intent classification
Spoken language understanding systems using audio-only data are gaining popularity, yet their ability to handle unseen intents remains limited. In this study, we propose a generalized zero-shot audio-to-intent classifica…
ClassificationGoal-Oriented Dialogintent-classificationIntent Classification+4Knowledge Distillation Meets Few-Shot Learning: An Approach for Few-Shot Intent Classification Within and Across Domains
Large Transformer-based natural language understanding models have achieved state-of-the-art performance in dialogue systems. However, scarce labeled data for training, the large model size, and low inference speed hinde…
Cross-Domain Few-Shotcross-domain few-shot learningFew-Shot Learningintent-classification+3A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanism
Zero-shot intent classification is a vital and challenging task in dialogue systems, which aims to deal with numerous fast-emerging unacquainted intents without annotated training data. To obtain more satisfactory perfor…
Classificationintent-classificationIntent ClassificationMeta-Learning+3