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Task-oriented dialogue systems in industry settings need to have highconversational capability, be easily adaptable to changing situations andconform to business constraints. This paper describes a 3-step procedure todevelop a conversational model that satisfies these criteria and canefficiently scale to rank a large set of response candidates. First, we providea simple algorithm to semi-automatically create a high-coverage template setfrom historic conversations without any annotation. Second, we propose a neuralarchitecture that encodes the dialogue context and applicable businessconstraints as profile features for ranking the next turn. Third, we describe atwo-stage learning strategy with self-supervised training, followed bysupervised fine-tuning on limited data collected through a human-in-the-loopplatform. Finally, we describe offline experiments and present results ofdeploying our model with human-in-the-loop to converse with live customersonline.

Deploying a Retrieval based Response Model for Task Oriented Dialogues