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Keyword: sequence learning ×Clear all

2 claims from 1 paper

1. Computer Science › Topic Modeling

   Semi-supervised Sequence Learning

   Dai and Le · arXiv (Cornell University) · 2015

   The paper presents two ways of using unlabelled data to pretrain recurrent networks, a language model and a sequence autoencoder, to improve later supervised sequence learning such as text classification.

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   1. UncheckedIn the authors' experiments, LSTM networks pretrained on unlabelled text with two methods were more stable and generalised better than those without pretraining.“In our experiments, we find that long short term memory recurrent networks after being pretrained with the two approaches are more stable and generalize better.”
   2. UncheckedPretraining on unlabelled text lets LSTM networks be trained on sequences a few hundred steps long and perform strongly on several text classification tasks.“With pretraining, we are able to train long short term memory recurrent networks up to a few hundred timesteps, thereby achieving strong performance in many text classification tasks, such as IMDB, DBpedia and 20 Newsgroups.”