Goal-oriented dialog has been given attention due to its numerous applications in artificial intelligence. Goal-oriented dialogue tasks occur when a questioner asks an action-oriented question and an answerer responds with the intent of letting the questioner know a correct action to take. To ask the adequate question, deep learning and reinforcement learning have been recently applied. However, these approaches struggle to find a competent recurrent neural questioner, owing to the complexity of learning a series of sentences. Motivated by theory of mind, we propose Answerer in Questioner's Mind (AQM), a novel information theoretic algorithm for goal-oriented dialog. With AQM, a questioner asks and infers based on an approximated probabilistic model of the answerer.

For MNIST, the approximated answerer is count-based and its recognition accuracy can be controlled proportional to the actual answerer's accuracy. For GuessWhat, the approximated answerer is trained in a variety of ways -- on the same training data as the actual answerer, on predicted answers from the actual answerer, on a different training data split as the actual answerer, and on a different training data split as the actual answerer followed by imitation of predicted answers on the other split. And the proposed approach outperforms the random baseline. Interestingly, the authors find that the depA* models perform better than the indA* models -- showing that training on predicted answers is a stronger signal for building an accurate mental model than just sharing training data. I'm happy to recommend this for publication.

Goal-oriented dialog has been given attention due to its numerous applications in artificial intelligence. Goal-oriented dialogue tasks occur when a questioner asks an action-oriented question and an answerer responds with the intent of letting the questioner know a correct action to take. To ask the adequate question, deep learning and reinforcement learning have been recently applied. However, these approaches struggle to find a competent recurrent neural questioner, owing to the complexity of learning a series of sentences. Motivated by theory of mind, we propose "Answerer in Questioner's Mind" (AQM), a novel information theoretic algorithm for goal-oriented dialog.