论文标题
通过序列混音正规化复发性神经网络
Regularizing Recurrent Neural Networks via Sequence Mixup
论文作者
论文摘要
在本文中，我们将最初针对前馈神经网络提出的一类著名的正规化技术扩展到了输入混合（Zhang等，2017）和歧管混合（Verma等，2018），以延伸到复发性神经网络（RNN）的领域。我们提出的方法易于实施，并且计算复杂性低，而在各种任务中都利用了简单的神经体系结构的性能。我们通过对现实世界数据集的几个实验验证了我们的主张，还提供了渐近理论分析，以进一步研究我们提出的技术的特性和潜在影响。将序列混音应用于Bilstm-CRF模型（Huang等，2015），以在CONLL-2003数据（Sang and de Meulder，2003）上命名的实体识别任务（2003年），在测试阶段提高了F-1分数，并大大降低了损失。
In this paper, we extend a class of celebrated regularization techniques originally proposed for feed-forward neural networks, namely Input Mixup (Zhang et al., 2017) and Manifold Mixup (Verma et al., 2018), to the realm of Recurrent Neural Networks (RNN). Our proposed methods are easy to implement and have a low computational complexity, while leverage the performance of simple neural architectures in a variety of tasks. We have validated our claims through several experiments on real-world datasets, and also provide an asymptotic theoretical analysis to further investigate the properties and potential impacts of our proposed techniques. Applying sequence mixup to BiLSTM-CRF model (Huang et al., 2015) to Named Entity Recognition task on CoNLL-2003 data (Sang and De Meulder, 2003) has improved the F-1 score on the test stage and reduced the loss, considerably.
