New Transfer Learning Techniques for Disparate Label Sets

Young-Bum Kim, Karl Stratos, Ruhi Sarikaya, Minwoo Jeong

Abstract: Examples of the present disclosure describe systems and methods of transfer learning techniques for disparate label sets. In aspects, a data set may be accessed on a server device. The data set may comprise labels and word sets associated with the labels. The server device may induce label embedding within the data set. The embedded labels may be represented by multi-dimensional vectors that correspond to particular labels. The vectors may be used to construct label mappings for the data set. The label mappings may be used to train a model to perform domain adaptation or transfer learning techniques. The model may be used to provide results to a statement/query or to train a different model.