Session #05: Deep Neural Networks with Multitask Learning

Presenter: Félix Grèzes
Date: February 5 2016 at 4:30pm
Location: CS Lab – Rm 4435
Links: A Unified Architecture for Natural Language Processing:Deep Neural Networks with Multitask Learning (Collobert and Weston, ICML 2008)
Paper Abstract: 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.

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 Supported by the CUNY Doctoral Students Council.  

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