NaRLE: Natural Language Models using Reinforcement Learning with Emotion Feedback

Understand

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.