To remedy these problems, we propose methods to train a joint model across the multiple tag-sets of the different datasets, sharing statistical strength by using a single feature encoder across datasets while respecting the incompleteness of the labels during training.
our single model can take full advantage of all the available annotated resources and predict the full set of relevant types given a piece of text.