Deep Reinforcement Learning for Dialogue Generation
This lesson is about the paper "Deep Reinforcement Learning for Dialogue Generation" by Jiwei Li, Will Monroe, Alan Ritter, Dan Jurafsky, Michel Galley, and Jianfeng Gao.
The paper was presented at the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP). The paper introduces a novel approach to dialogue generation using deep reinforcement learning, aiming to improve the quality and coherence of conversational agents.
The authors propose a model that combines reinforcement learning with sequence-to-sequence models to generate more contextually relevant and engaging responses. Through this lesson, students will learn about the challenges of dialogue generation and how deep reinforcement learning can be used to enhance the performance of conversational agents.
Read the paper here.
Happy learning!