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Task-oriented dialog presents a difficult challenge encompassing multipleproblems including multi-turn language understanding and generation, knowledgeretrieval and reasoning, and action prediction. Modern dialog systems typicallybegin by converting conversation history to a symbolic object referred to asbelief state by using supervised learning. The belief state is then used toreason on an external knowledge source whose result along with the conversationhistory is used in action prediction and response generation tasksindependently. Such a pipeline of individually optimized components not onlymakes the development process cumbersome but also makes it non-trivial toleverage session-level user reinforcement signals. In this paper, we developNeural Assistant: a single neural network model that takes conversation historyand an external knowledge source as input and jointly produces both textresponse and action to be taken by the system as output. The model learns toreason on the provided knowledge source with weak supervision signal comingfrom the text generation and the action prediction tasks, hence removing theneed for belief state annotations. In the MultiWOZ dataset, we study the effectof distant supervision, and the size of knowledge base on model performance. Wefind that the Neural Assistant without belief states is able to incorporateexternal knowledge information achieving higher factual accuracy scorescompared to Transformer. In settings comparable to reported baseline systems,Neural Assistant when provided with oracle belief state significantly improveslanguage generation performance.

Neural Assistant: Joint Action Prediction, Response Generation, and Latent Knowledge Reasoning