  In hope of expanding training data, researchers often want to merge two or
more datasets that are created using different labeling schemes. This paper
considers two datasets that label part-of-speech (POS) tags under different
tagging schemes and leverage the supervised labels of one dataset to help
generate labels for the other dataset. This paper further discusses the
theoretical difficulties of this approach and proposes a novel supervised
architecture employing Transformers to tackle the problem of consolidating two
completely disjoint datasets. The results diverge from initial expectations and
discourage exploration into the use of disjoint labels to consolidate datasets
with different labels.
