Dynamic conversational responses are generated by receiving a first user action during a conversational interaction with a user interface 104, determining a first feature input based on this action, retrieving a plurality of intent clusters which are generated by a first machine learning model through unsupervised hierarchical clustering, preferably by clustering into hierarchies of correlation-distances between intents, inputting the first feature input into a second machine learning model which selects a subset of the intent clusters based on the feature input wherein each intent cluster corresponds to a respective intent of the user following the action, selecting, based on the output of the second machine learning model, a dynamic conversational response 102 that includes a respective option 106/108 for each intent in the subset, and generating the response at the user interface. The second machine learning model may be selected from a plurality based on the intent clusters and the first machine learning model may use a matrix of pairwise correlations. Following a second user action a second feature input may be determined and used to generate a different dynamic response. The subset of intent clusters may be based on the screen size.
Systems and methods for generating training data for sequential conversational responses