A unified architecture for natural language processing: deep neural networks with multitask learning

We describe a single convolutional neural network architecture that, given a sentence, outputs a host of language processing predictions: part-of-speech tags, chunks, named entity tags, semantic roles, semantically similar words and the likelihood that the sentence makes sense (grammatically and semantically) using a language model. The entire network is trained jointly on all these tasks using weight-sharing, an instance of multitask learning. All the tasks use labeled data except the language model which is learnt from unlabeled text and represents a novel form of semi-supervised learning for the shared tasks. We show how both multitask learning and semi-supervised learning improve the generalization of the shared tasks, resulting in stateof-the-art performance.

• Type: Conference
• Year: 2008
• Where: ICML
• Authors: Ronan Collobert, Jason Weston
• Keywords: ICML 2008, Machine Learning, Multitask Learning, Semi-supervised Learning, Sentence Makes Sense