Every day, millions of people use natural language interfaces in virtual digital assistants such as Amazon Alexa, Apple’s Siri, Google, Microsoft Cortana, Samsung’s Bixby and Facebook Potal via in-home devices or phones. At the same time, interest among the NLP research community in conversational systems has blossomed to the extent that Dialogue and Interactive Systems is consistently among the top three tracks in NLP conferences receiving a record number of submissions. Today’s industrial conversational AI systems are built using the traditional NLP pipeline, i.e., natural language understanding, dialog state tracking, dialog policy, and natural language generation. Despite its success, this pipeline fundamentally limits performance, humanness, and scaling of conversational AI systems. To overcome these challenges, dialog researchers have started embracing end-to-end neural approaches for the next generation of conversational AI systems, as such approaches have been setting state-of-the-art performance records on several NLP tasks. However, Neural Conversational AI systems are still far from shippable in the real world. We identify the following main outstanding questions to bridge this gap:
- Grounding in external systems
- Safety/integrity/robustness
- Continual learning

The goal of this workshop is to bring together machine learning researchers and dialog researchers from academia and industry to encourage knowledge transfer and collaboration in this space with the goal of bridging the gap between research and real world use cases in neural approaches to Conversational AI. The ideal outcome of the workshop is to identify a set of concrete research directions for the research community (both NLP and representation learning communities) to enable the next generation of digital assistants via Neural Conversational AI systems. We will make the findings from this workshop broadly available to the research community.