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Multitask Learning for Sequence Labeling Tasks
[article]
2016
arXiv
pre-print
In this paper, we present a learning method for sequence labeling tasks in which each example sequence has multiple label sequences. Our method learns multiple models, one model for each label sequence. Each model computes the joint probability of all label sequences given the example sequence. Although each model considers all label sequences, its primary focus is only one label sequence, and therefore, each model becomes a task-specific model, for the task belonging to that primary label.
arXiv:1404.6580v2
fatcat:4gl4mnrtrfarxmi5gtd22f3ovy