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In hope of expanding training data, researchers often want to merge two ormore datasets that are created using different labeling schemes. This paperconsiders two datasets that label part-of-speech (POS) tags under differenttagging schemes and leverage the supervised labels of one dataset to helpgenerate labels for the other dataset. This paper further discusses thetheoretical difficulties of this approach and proposes a novel supervisedarchitecture employing Transformers to tackle the problem of consolidating twocompletely disjoint datasets. The results diverge from initial expectations anddiscourage exploration into the use of disjoint labels to consolidate datasetswith different labels.

TransPOS: Transformers for Consolidating Different POS Tagset Datasets