Abstract: Methods and systems are described herein for merging datasets from multiple content domains for training a prediction model to predict a solution for a request related to a specific content domain. A dataset for a content domain may include requests and solutions organized as groups. For example, a dataset for a first content domain may include a first group having (a) a first set of requests (e.g., questions or queries) related to a first topic, and (h) a solution (e.g., an answer) associated with the first set of requests. The datasets of different content domains are analyzed based on context-based vector representations of the requests or solutions to determine the groups that are similar and merge those similar groups into a single merged group. A prediction model is trained with the merged groups for obtaining a prediction of a solution to any given request.

Abstract: Methods and systems are described herein for merging datasets from multiple content domains for training a prediction model to predict a solution for a request related to a specific content domain. A dataset for a content domain may include requests and solutions organized as groups. For example, a dataset for a first content domain may include a first group having (a) a first set of requests (e.g., questions or queries) related to a first topic, and (h) a solution (e.g., an answer) associated with the first set of requests. The datasets of different content domains are analyzed based on context-based vector representations of the requests or solutions to determine the groups that are similar and merge those similar groups into a single merged group. A prediction model is trained with the merged groups for obtaining a prediction of a solution to any given request.