Identifying beneficial task relations for multi-task learning in deep neural networks

Anders S{\o}gaard; Joachim Bingel

Identifying beneficial task relations for multi-task learning in deep neural networks

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arxiv 1702.08303 v1 pith:45V7W4ZO submitted 2017-02-27 cs.CL

Identifying beneficial task relations for multi-task learning in deep neural networks

Joachim Bingel , Anders S{\o}gaard This is my paper

classification cs.CL

keywords deepgainslearningmodelsmulti-tasknetworksneuralrelations

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Multi-task learning (MTL) in deep neural networks for NLP has recently received increasing interest due to some compelling benefits, including its potential to efficiently regularize models and to reduce the need for labeled data. While it has brought significant improvements in a number of NLP tasks, mixed results have been reported, and little is known about the conditions under which MTL leads to gains in NLP. This paper sheds light on the specific task relations that can lead to gains from MTL models over single-task setups.

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