Open AccessProceedings Article
Semi-supervised Sequence Learning
Andrew M. Dai,Quoc V. Le +1 more
- Vol. 28, pp 3079-3087
TLDR
Two approaches to use unlabeled data to improve Sequence Learning with recurrent networks are presented and it is found that long short term memory recurrent networks after pretrained with the two approaches become more stable to train and generalize better.Abstract:
We present two approaches to use unlabeled data to improve Sequence Learning with recurrent networks. The first approach is to predict what comes next in a sequence, which is a language model in NLP. The second approach is to use a sequence autoencoder, which reads the input sequence into a vector and predicts the input sequence again. These two algorithms can be used as a "pretraining" algorithm for a later supervised sequence learning algorithm. In other words, the parameters obtained from the pretraining step can then be used as a starting point for other supervised training models. In our experiments, we find that long short term memory recurrent networks after pretrained with the two approaches become more stable to train and generalize better. With pretraining, we were able to achieve strong performance in many classification tasks, such as text classification with IMDB, DBpedia or image recognition in CIFAR-10.read more
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