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Neural-based models have achieved outstanding performance on slot filling andintent classification, when fairly large in-domain training data are available.However, as new domains are frequently added, creating sizeable data isexpensive. We show that lightweight augmentation, a set of augmentation methodsinvolving word span and sentence level operations, alleviates data scarcityproblems. Our experiments on limited data settings show that lightweightaugmentation yields significant performance improvement on slot filling on theATIS and SNIPS datasets, and achieves competitive performance with respect tomore complex, state-of-the-art, augmentation approaches. Furthermore,lightweight augmentation is also beneficial when combined with pre-trainedLM-based models, as it improves BERT-based joint intent and slot fillingmodels.

Simple is Better! Lightweight Data Augmentation for Low Resource Slot Filling and Intent Classification