  We propose a novel dialogue modeling framework, the first-ever nonparametric
kernel functions based approach for dialogue modeling, which learns kernelized
hashcodes as compressed text representations; unlike traditional deep learning
models, it handles well relatively small datasets, while also scaling to large
ones. We also derive a novel lower bound on mutual information, used as a
model-selection criterion favoring representations with better alignment
between the utterances of participants in a collaborative dialogue setting, as
well as higher predictability of the generated responses. As demonstrated on
three real-life datasets, including prominently psychotherapy sessions, the
proposed approach significantly outperforms several state-of-art neural network
based dialogue systems, both in terms of computational efficiency, reducing
training time from days or weeks to hours, and the response quality, achieving
an order of magnitude improvement over competitors in frequency of being chosen
as the best model by human evaluators.
