Multi-Task Learning for Argumentation Mining in Low-Resource Settings
Computation and Language
2018-05-07 v3
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.
Cite
@article{arxiv.1804.04083,
title = {Multi-Task Learning for Argumentation Mining in Low-Resource Settings},
author = {Claudia Schulz and Steffen Eger and Johannes Daxenberger and Tobias Kahse and Iryna Gurevych},
journal= {arXiv preprint arXiv:1804.04083},
year = {2018}
}
Comments
Accepted at NAACL 2018