Goal-oriented conversation with code-mixed language

A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a device to cause the device to:

• create a language model corpus including multilingual alignment for training a combined language model, the language model corpus created from data sourced from a computer network and train the combined language model;
• create an adverse medication reaction corpus by analyzing data from online sources which include adverse medication reaction utterances and labeling an N-gram of an utterance in the adverse medication reaction utterances as a response to query;
• and generate a code-mixed utterance model to perform code-mixed utterances in a turn by turn dialogue, by at least adding additional output layer including at least a start vector, language vector, and a query vector including at least the labeled N-gram, which are additional to the combined language model's predicted next words, wherein the start vector encodes what word should start a response to an input utterance, and the language vector encodes what language out of multiple languages used in the code-mixed utterances should a next word of the response be in, wherein the code-mixed utterances represent utterances performed using multiple languages in a mixed manner in a conversation;
• and wherein the device is caused to align text from multiple languages by adversarial learning of a linear mapping between the multiple languages in an embedding space and refining alignments using geometric transformation.

A computer-implemented method comprising:

• creating a language model corpus including multilingual alignment for training a combined language model, the language model corpus created from data sourced from a computer network and train the combined language model;
• creating an adverse medication reaction corpus by analyzing data from online sources which include adverse medication reaction utterances and labeling an N-gram of an utterance in the adverse medication reaction utterances as a response to query;
• and generating a code-mixed utterance model to perform code-mixed utterances in a turn by turn dialogue, by at least adding additional output layer including at least a start vector, language vector, and a query vector including at least the labeled N-gram, which are additional to the combined language model's predicted next words, wherein the start vector encodes what word should start a response to an input utterance, and the language vector encodes what language out of multiple languages used in the code-mixed utterances should a next word of the response be in, wherein the code-mixed utterances represent utterances performed using multiple languages in a mixed manner in a conversation;
• and aligning text from multiple languages by adversarial learning of a linear mapping between the multiple languages in an embedding space and refining alignments using geometric transformation.