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

By Samuel Louvan, Bernardo Magnini. 2020

31 citations recorded by the source corpus.

Neural-based models have achieved outstanding performance on slot filling and intent classification, when fairly large in-domain training data are available. However, as new domains are frequently added, creating sizeable data is expensive. We show that lightweight augmentation, a set of augmentation methods involving word span and sentence level operations, alleviates data scarcity problems. Our experiments on limited data settings show that lightweight augmentation yields significant performance improvement on slot filling on the ATIS and SNIPS datasets, and achieves competitive performance with respect to more complex, state-of-the-art, augmentation approaches. Furthermore, lightweight augmentation is also beneficial when combined with pre-trained LM-based models, as it improves BERT-based joint intent and slot filling models.

Subfields: Artificial Intelligence, Natural Language Processing, Dialogue & Conversational AI

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Wanxiang Che
2. Bohan Li
3. Yutai Hou

In Natural Language Processing

1. Wanxiang Che
2. Bohan Li
3. Yutai Hou

Citations in the corpus

• A Survey of Data Augmentation Approaches for NLP (2021, neutral): “, 2020) × 3 tok Input+Label × SLOT-SUB-LM (Louvan and Magnini, 2020) × 3 tok Input 3 UBT & TBT (Vaibhav et al.”
• Data Augmentation Approaches in Natural Language Processing: A Survey (2021, background): “Louvan et al. [11] crop particular fragments on the dependency tree to create a smaller sentence.”