MATCHING BASED INTENT UNDERSTANDING WITH TRANSFER LEARNING

Described herein is a mechanism to identify user intent in requests submitted to a system such as a digital assistant or question-answer systems. Embodiments utilize a match methodology instead of a classification methodology. Features derived from a subgraph retrieved from a knowledge base based on the request are concatenated with pretrained word embeddings for both the request and a candidate predicate. The concatenated inputs for both the request and predicate are encoded using two independent LSTM networks and then a matching score is calculated using a match LSTM network. The result is identified based on the matching scores for a plurality of candidate predicates. The pretrained word embeddings allow for knowledge transfer since pretrained word embeddings in one intent domain can apply to another intent domain without retraining.