论文标题
简单更好！低资源插槽填充和意图分类的轻量级数据增强
Simple is Better! Lightweight Data Augmentation for Low Resource Slot Filling and Intent Classification
论文作者
论文摘要
当提供相当大的内域培训数据时，基于神经的模型在插槽填充和意图分类方面取得了出色的性能。但是，随着新域经常添加，创建大量数据很昂贵。我们表明，轻巧的增强是一组涉及单词跨度和句子级别操作的增强方法，可以减轻数据稀缺问题。我们对有限数据设置的实验表明，轻巧的增强功能可在ATIS和SNIPS数据集上的老虎机填充方面显着提高性能，并就更复杂，最先进的，最先进的增强方法实现竞争性能。此外，与预培训的基于LM的模型结合使用，轻巧的增强也是有益的，因为它改善了基于BERT的关节意图和插槽填充模型。
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.
