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Deconstructing Supertagging into Multi-Task Sequence Prediction
2019
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
Supertagging is a sequence prediction task where each word is assigned a complex syntactic structure called a supertag. In this thesis, we propose a novel multi-task learning approach for Tree Adjoining Grammar (TAG) supertagging by deconstructing these complex supertags to a set of related but auxiliary sequence prediction tasks, which can best represent the structural information of each supertag. Our multi-task prediction framework is trained over the same training data used to train the
doi:10.18653/v1/k19-1002
dblp:conf/conll/ZhuS19
fatcat:lpnyd63yp5bgjhnyw4hoe6ia4q