论文标题
变形金刚作为神经增强器：通过变异贝叶斯产生的有条件句子
Transformers as Neural Augmentors: Class Conditional Sentence Generation via Variational Bayes
论文作者
论文摘要
近年来探索了自然语言处理任务的数据增强方法，但是它们是有限的，很难捕获句子级别的多样性。此外，并非总是可以对监督任务执行数据增强。为了解决这些问题，我们提出了一种神经数据增强方法，该方法是条件变异自动编码器和编码器二次变压器模型的组合。在编码和解码输入句子时，我们的模型捕获了及其类条件的输入语言的句法和语义表示。在过去几年的发展方面的发展模型之后，我们在几个基准上训练和评估了我们的模型，以加强下游任务。我们将方法与3种不同的增强技术进行比较。提出的结果表明，与其他数据增强技术相比，我们的模型增加了当前模型的性能。
Data augmentation methods for Natural Language Processing tasks are explored in recent years, however they are limited and it is hard to capture the diversity on sentence level. Besides, it is not always possible to perform data augmentation on supervised tasks. To address those problems, we propose a neural data augmentation method, which is a combination of Conditional Variational Autoencoder and encoder-decoder Transformer model. While encoding and decoding the input sentence, our model captures the syntactic and semantic representation of the input language with its class condition. Following the developments in the past years on pre-trained language models, we train and evaluate our models on several benchmarks to strengthen the downstream tasks. We compare our method with 3 different augmentation techniques. The presented results show that, our model increases the performance of current models compared to other data augmentation techniques with a small amount of computation power.
