End-to-end model for multi-turn conversational dialogue agent

Conversational dialogue has been researched for many years, especially with the advert of deep learning (DL). Researchers tend to introduce DL into dialogue construction. Historically, the approaches for conversational dialogue evolved from rule-based to machine learning based. Due to the popularity of sequence to sequence model, the quality of conversational dialogue improved significantly. However, there are still many challenges that need to be conquered, including multi-turn dialogue, consistent persona, and evaluation method. In this project, I mainly focus on these challenges, aiming at proposing a novel end-to-end model to implement a multi-turn conversational dialogue agent.