@inproceedings{schulz-etal-2018-multi,
title = "Multi-Task Learning for Argumentation Mining in Low-Resource Settings",
author = "Schulz, Claudia and
Eger, Steffen and
Daxenberger, Johannes and
Kahse, Tobias and
Gurevych, Iryna",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2006",
doi = "10.18653/v1/N18-2006",
pages = "35--41",
abstract = "We investigate whether and where multi-task learning (MTL) can improve performance on NLP problems related to argumentation mining (AM), in particular argument component identification. Our results show that MTL performs particularly well (and better than single-task learning) when little training data is available for the main task, a common scenario in AM. Our findings challenge previous assumptions that conceptualizations across AM datasets are divergent and that MTL is difficult for semantic or higher-level tasks.",
}
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<abstract>We investigate whether and where multi-task learning (MTL) can improve performance on NLP problems related to argumentation mining (AM), in particular argument component identification. Our results show that MTL performs particularly well (and better than single-task learning) when little training data is available for the main task, a common scenario in AM. Our findings challenge previous assumptions that conceptualizations across AM datasets are divergent and that MTL is difficult for semantic or higher-level tasks.</abstract>
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%0 Conference Proceedings
%T Multi-Task Learning for Argumentation Mining in Low-Resource Settings
%A Schulz, Claudia
%A Eger, Steffen
%A Daxenberger, Johannes
%A Kahse, Tobias
%A Gurevych, Iryna
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F schulz-etal-2018-multi
%X We investigate whether and where multi-task learning (MTL) can improve performance on NLP problems related to argumentation mining (AM), in particular argument component identification. Our results show that MTL performs particularly well (and better than single-task learning) when little training data is available for the main task, a common scenario in AM. Our findings challenge previous assumptions that conceptualizations across AM datasets are divergent and that MTL is difficult for semantic or higher-level tasks.
%R 10.18653/v1/N18-2006
%U https://aclanthology.org/N18-2006
%U https://doi.org/10.18653/v1/N18-2006
%P 35-41
Markdown (Informal)
[Multi-Task Learning for Argumentation Mining in Low-Resource Settings](https://aclanthology.org/N18-2006) (Schulz et al., NAACL 2018)
ACL
- Claudia Schulz, Steffen Eger, Johannes Daxenberger, Tobias Kahse, and Iryna Gurevych. 2018. Multi-Task Learning for Argumentation Mining in Low-Resource Settings. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 35–41, New Orleans, Louisiana. Association for Computational Linguistics.