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C2C-GenDA: Cluster-to-Cluster Generation for Data Augmentation of Slot Filling
Slot filling, a fundamental module of spoken language understanding, oft...
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Few-shot Learning for Multi-label Intent Detection
In this paper, we study the few-shot multi-label classification for user...
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FewJoint: A Few-shot Learning Benchmark for Joint Language Understanding
Few-learn learning (FSL) is one of the key future steps in machine learn...
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Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network
In this paper, we explore the slot tagging with only a few labeled suppo...
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Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting
Deep pretrained language models have achieved great success in the way o...
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A Corpus-free State2Seq User Simulator for Task-oriented Dialogue
Recent reinforcement learning algorithms for task-oriented dialogue syst...
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Few-Shot Sequence Labeling with Label Dependency Transfer
Few-shot sequence labeling faces a unique challenge compared with the ot...
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Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding
In this paper, we study the problem of data augmentation for language un...
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