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.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system comprising: at least one processor; and memory coupled to the at least one processor, the memory comprising computer executable instructions that, when executed by the at least one processor, performs a method comprising: accessing a first set of labeled data; transforming the first set of labeled data into a set of vectors; identifying a commonality in a label among at least two vectors from the set of vectors; based on the identified commonality, generating a coarse label set using the at least two vectors; training a model using the coarse label set; and using the trained model, mapping a first label associated with the coarse label set to a second label associated with a second set of labeled data. 2. The system of claim 1, further comprising: receiving the labeled data from a received input, wherein the labeled data comprises query data associated with the received input. 3. The system of claim 2, further comprising: using the model to provide a result set of a query to the user. 4. The system of claim 1, wherein transforming the first set of labeled data comprises applying canonical correlation analysis (CCA) to one or more labels using the first set of labeled data. 5. The system of claim 1, wherein generating the coarse label set comprises aggregating the at least two vectors using at least one technique selected from the group consisting of k-means clustering, spectral clustering, affinity propagation, mean-shift, Ward hierarchical clustering, agglomerative clustering, DBSCAN, Gaussian mixtures, and Birch clustering. 6. The system of claim 1, wherein the coarse label set is generalized from the at least two vectors. 7. The system of claim 1, wherein training the model comprises identifying one or more labels that are semantically related to the coarse label set. 8. The system of claim 6, further comprising predicting labels for a target domain using the identified one or more labels. 9. The system of claim 1, wherein the second label shares the commonality of the at least two vectors. 10. A system for mapping disparate label sets, the system comprising: at least one processor; and memory coupled to the at least one processor, the memory comprising computer executable instructions that, when executed by the at least one processor, performs a method comprising: accessing a set of labeled data, wherein the set of labeled data comprises data from a first domain and data from a second domain; transforming the set of labeled data into a set of vectors; identifying, in the set of vectors, a first vector comprising data from the first domain and a second vector comprising data from the second domain, wherein the first vector shares a commonality with the second vector; mapping the first vector to the second vector; and training a model using at least the second vector. 11. The system of claim 10, further comprising: receiving the labeled data from a user, wherein the labeled data comprises query data associated with the user. 12. The system of claim 10, wherein transforming the first set of labeled data comprises applying canonical correlation analysis (CCA) to the first set of labeled data. 13. The system of claim 10, wherein the commonality represents that the first vector is a nearest match in the data from the first domain to the second vector, wherein the nearest match is determined using a k-nearest neighbor algorithm. 14. The system of claim 10, wherein mapping comprises generating a bijective mapping between the first vector and the second vector. 15. The system of claim 10, wherein training the model comprises duplicating the set of vectors. 16. The system of claim 15, further comprising conjoining each of the vectors in the duplicated set of vectors with a domain indicator. 17. The system of claim 10, further comprising: using the model to generate a result set. 18. A computer-implemented method for mapping disparate label sets, the method comprising: accessing, on a device, a first set of labeled data; transforming the first set of labeled data into a set of vectors; identifying a commonality in a label among at least two vectors from the set of vectors; based on the identified commonality, generating a coarse label set using the at least two vectors; training a model using the coarse label set; and using the trained model, mapping a first label associated with the coarse label set to a second label associated with a second set of labeled data. 19. The computer-implemented method of claim 18, further comprising: receiving the labeled data from a user, wherein the labeled data comprises query data associated with the user; using the model to generate a result set from the query data; and providing the result set to the user. 20. The computer-implemented method of claim 18, wherein transforming the first set of labeled data comprises applying canonical correlation analysis (CCA) to the first set of labeled data.