  Existing studies in conversational AI mostly treat task-oriented dialog (TOD)
and question answering (QA) as separate tasks. Towards the goal of constructing
a conversational agent that can complete user tasks and support information
seeking, it is important to build a system that handles both TOD and QA with
access to various external knowledge. In this work, we propose a new task,
Open-Book TOD (OB-TOD), which combines TOD with QA task and expand external
knowledge sources to include both explicit knowledge sources (e.g., the Web)
and implicit knowledge sources (e.g., pre-trained language models). We create a
new dataset OB-MultiWOZ, where we enrich TOD sessions with QA-like information
seeking experience grounded on external knowledge. We propose a unified model
OPERA (Open-book End-to-end Task-oriented Dialog) which can appropriately
access explicit and implicit external knowledge to tackle the defined task.
Experimental results demonstrate OPERA's superior performance compared to
closed-book baselines and illustrate the value of both knowledge types.
