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We study conversational dialog in which there are many possible responses toa given history. We present the MultiTalk Dataset, a corpus of over 320,000sentences of written conversational dialog that balances a high branchingfactor (10) with several conversation turns (6) through selective branchcontinuation. We make multiple contributions to study dialog generation in thehighly branching setting. In order to evaluate a diverse set of generations, wepropose a simple scoring algorithm, based on bipartite graph matching, tooptimally incorporate a set of diverse references. We study multiple languagegeneration tasks at different levels of predictive conversation depth, usingtextual attributes induced automatically from pretrained classifiers. Ourculminating task is a challenging theory of mind problem, a controllablegeneration task which requires reasoning about the expected reaction of thelistener.

MultiTalk: A Highly-Branching Dialog Testbed for Diverse Conversations