  Current research in dialogue systems is focused on conversational assistants
working on short conversations in either task-oriented or open domain settings.
In this paper, we focus on improving task-based conversational assistants
online, primarily those working on document-type conversations (e.g., emails)
whose contents may or may not be completely related to the assistant's task. We
propose "NARLE" a deep reinforcement learning (RL) framework for improving the
natural language understanding (NLU) component of dialogue systems online
without the need to collect human labels for customer data. The proposed
solution associates user emotion with the assistant's action and uses that to
improve NLU models using policy gradients. For two intent classification
problems, we empirically show that using reinforcement learning to fine tune
the pre-trained supervised learning models improves performance up to 43%.
Furthermore, we demonstrate the robustness of the method to partial and noisy
implicit feedback.
