Towards Deep Understanding in Task-Oriented Dialogue Systems Using Deep Reinforcement Learning

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Conversation is a natural way for humans to obtain and absorb information. Several generations of artificial conversational agents have been developed since the 1960's, but we are still a long way from dialog systems which can respond as flexibly and usefully as a human can. Supervised end-to-end Deep Learning (DL) approaches overcome several limitations of previous pipeline methods, but still require large training sets and may suffer from a lack of concept understanding due to an inability to explore beyond the training data. This work intends to deliver improvements in task-oriented dialog systems by incorporating grounded language techniques into Deep Reinforcement Learning (DRL) agents, trained end-to-end in text-based simulations such as the bAbI benchmark [1].