Non-limiting examples of the present disclosure describe a convolutional neural network (CNN) architecture configured to evaluate conversational relevance of query-response pairs. A CNN model is provided. The CNN model comprises: a first branch, a second branch, and multilayer perceptron (MLP) layers. The first branch comprises a plurality of convolutional layers with dynamic pooling to process a query. The second branch comprises a plurality of convolutional layers with dynamic pooling to process candidate responses for the query. The MLP layers are configured to rank query-response pairs based on conversational relevance. The query and the candidate responses are processed in parallel using the CNN model. Pairwise ranking of the query-response pairs are generated using the MLP layers based on a first input propagated from the first branch and second input propagated from the second branch. A ranking of one or more query-response pairs may be output. Other examples are also described.
CONVERSATIONAL RELEVANCE MODELING USING CONVOLUTIONAL NEURAL NETWORK