In our case, this sharing is between two models: On one hand, a standard Sequence-to-Sequence conversational models is trained to predict the current response given the previous context.
using the non-conversational data, we introduce an autoencoder multi-task learning strategy that predicts the response given the same sequence, but with the target parameters tied with the general conversational model.