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: receiving input including at least one of voice, visual, touch and text input associated with a statement or query via a user interface component or other input means; identifying two or more domains, labels and data sets within the input and identifying associations between the domains, labels and data sets; storing the identified domains, labels and data sets and/or their associations within a data store; accessing a first set of labeled data, wherein the set of labeled data comprises data from a first domain and data from a second domain; transforming the first 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; 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; mapping the first vector to the second vector; training a model using the coarse label set and at least the second vector; 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 from the second domain; and using the model trained on the second domain to provide results to the statement or query on the first and/or second domain. 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: receiving input including at least one of voice, visual, touch and text input associated with a statement or query via a user interface component or other input means; identifying two or more domains, labels and data sets within the input and identifying associations between the domains, labels and data sets; storing the identified domains, labels and data sets and/or their associations within a data store; accessing a first set of labeled data, wherein the set of labeled data comprises data from a first domain and data from a second domain; transforming the first 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; 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; mapping the first vector to the second vector; training a model using the coarse label set and at least the second vector; 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 from the second domain; and using the model trained on the second domain to provide results to the statement or query on the first and/or second domain. receiving input including at least one of voice, visual, touch and text input associated with a statement or query via a user interface component or other input means; identifying two or more domains, labels and data sets within the input and identifying associations between the domains, labels and data sets; storing the identified domains, labels and data sets and/or their associations within a data store; accessing a first set of labeled data, wherein the set of labeled data comprises data from a first domain and data from a second domain; transforming the first 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; 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; mapping the first vector to the second vector; training a model using the coarse label set and at least the second vector; 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 from the second domain; and using the model trained on the second domain to provide results to the statement or query on the first and/or second domain.