{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/semi-supervised-sequence-learning","title":"Semi-supervised Sequence Learning","arxiv_id":"1511.01432","date":"2015-11-04","proceeding":"NeurIPS 2015 12","authors":["Andrew M. Dai","Quoc V. Le"],"abstract":"We present two approaches that use unlabeled data to improve sequence\nlearning with recurrent networks. The first approach is to predict what comes\nnext in a sequence, which is a conventional language model in natural language\nprocessing. The second approach is to use a sequence autoencoder, which reads\nthe input sequence into a vector and predicts the input sequence again. These\ntwo algorithms can be used as a \"pretraining\" step for a later supervised\nsequence learning algorithm. In other words, the parameters obtained from the\nunsupervised step can be used as a starting point for other supervised training\nmodels. In our experiments, we find that long short term memory recurrent\nnetworks after being pretrained with the two approaches are more stable and\ngeneralize better. With pretraining, we are able to train long short term\nmemory recurrent networks up to a few hundred timesteps, thereby achieving\nstrong performance in many text classification tasks, such as IMDB, DBpedia and\n20 Newsgroups.","url_abs":"http://arxiv.org/abs/1511.01432v1","url_pdf":"http://arxiv.org/pdf/1511.01432v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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