%0 Generic %1 alonso2016multitask %A Alonso, Héctor Martínez %A Plank, Barbara %D 2016 %K auxilliary deep_learning multitask nlp %T When is multitask learning effective? Semantic sequence prediction under varying data conditions %U http://arxiv.org/abs/1612.02251 %X Multitask learning has been applied successfully to a range of tasks, mostly morphosyntactic. However, little is known on when MTL works and whether there are data characteristics that help to determine its success. In this paper we evaluate a range of semantic sequence labeling tasks in a MTL setup. We examine different auxiliary tasks, amongst which a novel setup, and correlate their impact to data-dependent conditions. Our results show that MTL is not always effective, significant improvements are obtained only for 1 out of 5 tasks. When successful, auxiliary tasks with compact and more uniform label distributions are preferable.

@misc{alonso2016multitask, abstract = {Multitask learning has been applied successfully to a range of tasks, mostly morphosyntactic. However, little is known on when MTL works and whether there are data characteristics that help to determine its success. In this paper we evaluate a range of semantic sequence labeling tasks in a MTL setup. We examine different auxiliary tasks, amongst which a novel setup, and correlate their impact to data-dependent conditions. Our results show that MTL is not always effective, significant improvements are obtained only for 1 out of 5 tasks. When successful, auxiliary tasks with compact and more uniform label distributions are preferable.}, added-at = {2018-04-27T12:02:18.000+0200}, author = {Alonso, Héctor Martínez and Plank, Barbara}, biburl = {https://www.bibsonomy.org/bibtex/27384fde3f21e0f831818a156582ca105/dallmann}, description = {When is multitask learning effective? Semantic sequence prediction under varying data conditions}, interhash = {ae614914fd7105b629b565dc65258639}, intrahash = {7384fde3f21e0f831818a156582ca105}, keywords = {auxilliary deep_learning multitask nlp}, note = {cite arxiv:1612.02251Comment: In EACL 2017}, timestamp = {2018-04-27T12:02:18.000+0200}, title = {When is multitask learning effective? Semantic sequence prediction under varying data conditions}, url = {http://arxiv.org/abs/1612.02251}, year = 2016 }