Data Augmentation with Atomic Templates for Spoken Language Understanding

Understand

Spoken Language Understanding (SLU) converts user utterances into structured semantic representations.

• Data sparsity is one of the main obstacles of SLU due to the high cost of human annotation, especially when domain changes or a new domain comes.
• In this work, we propose a data augmentation method with atomic templates for SLU, which involves minimum human efforts.
• The atomic templates produce exemplars for fine-grained constituents of semantic representations.