  We study conversational dialog in which there are many possible responses to
a given history. We present the MultiTalk Dataset, a corpus of over 320,000
sentences of written conversational dialog that balances a high branching
factor (10) with several conversation turns (6) through selective branch
continuation. We make multiple contributions to study dialog generation in the
highly branching setting. In order to evaluate a diverse set of generations, we
propose a simple scoring algorithm, based on bipartite graph matching, to
optimally incorporate a set of diverse references. We study multiple language
generation tasks at different levels of predictive conversation depth, using
textual attributes induced automatically from pretrained classifiers. Our
culminating task is a challenging theory of mind problem, a controllable
generation task which requires reasoning about the expected reaction of the
listener.
