Building open domain conversational systems that allow users to have engaging conversations on topics of their choice is a challenging task. The Alexa Prize Socialbot Grand Challenge was launched in 2016 to tackle the problem of achieving natural, sustained, coherent and engaging open-domain dialogs. In the fourth iteration of the competition, university teams have incorporated semantic parsing, common sense reasoning, personalization, neural response generation, as well as novel response ranking models into the state of the art. The Fourth Socialbot Grand Challenge included an improved version of the CoBot (conversational bot) toolkit from the prior competition, along with upgraded topic and intent classifiers, BERTbased named entity recognition model, a punctuation model that injects punctuation marks into the ASR output, and a new neural response generator trained on conversations with Alexa Let’s Chat. This paper outlines the advances developed by the university teams as well as the Alexa Prize team to move closer to the Grand Challenge objective, including open domain natural language understanding, commonsense reasoning, dialog management, neural response generation, and dialog evaluation. As of the end of the final feedback phase, the top 7-day average rating achieved by a socialbot was 3.56, with the top 90th percentile conversation duration of 12 minutes 7 seconds.