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            <namePart type="given">Mirella</namePart>
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    <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 the success of MTL. In this paper we evaluate a range of semantic sequence labeling tasks in a MTL setup. We examine different auxiliary task configurations, amongst which a novel setup, and correlate their impact to data-dependent conditions. Our results show that MTL is not always effective, because significant improvements are obtained only for 1 out of 5 tasks. When successful, auxiliary tasks with compact and more uniform label distributions are preferable.</abstract>
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%0 Conference Proceedings
%T When is multitask learning effective? Semantic sequence prediction under varying data conditions
%A Martínez Alonso, Héctor
%A Plank, Barbara
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F martinez-alonso-plank-2017-multitask
%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 the success of MTL. In this paper we evaluate a range of semantic sequence labeling tasks in a MTL setup. We examine different auxiliary task configurations, amongst which a novel setup, and correlate their impact to data-dependent conditions. Our results show that MTL is not always effective, because significant improvements are obtained only for 1 out of 5 tasks. When successful, auxiliary tasks with compact and more uniform label distributions are preferable.
%U https://aclanthology.org/E17-1005
%P 44-53

Markdown (Informal)

[When is multitask learning effective? Semantic sequence prediction under varying data conditions](https://aclanthology.org/E17-1005) (Martínez Alonso & Plank, EACL 2017)

• When is multitask learning effective? Semantic sequence prediction under varying data conditions (Martínez Alonso & Plank, EACL 2017)

ACL

• Héctor Martínez Alonso and Barbara Plank. 2017. When is multitask learning effective? Semantic sequence prediction under varying data conditions. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 44–53, Valencia, Spain. Association for Computational Linguistics.