Multi-Task Learning for Sequence Tagging: An Empirical Study

COLING 2018 Soravit ChangpinyoHexiang HuFei Sha

We study three general multi-task learning (MTL) approaches on 11 sequence tagging tasks. Our extensive empirical results show that in about 50% of the cases, jointly learning all 11 tasks improves upon either independent or pairwise learning of the tasks... (read more)

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