1 research outputs found
Multi-Task Learning for Argumentation Mining in Low-Resource Settings
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.Comment: Accepted at NAACL 201