Speaker:Miaoran Li

Harmonizing Task-Oriented Dialogs and Information Seeking Experience

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.

Committee: Qi Li (co-major professor), Zhu Zhang (co-major professor), Forrest Bao, Hongyang Gao, and Mengdi Huai

Join on WebEx: https://iastate.webex.com/iastate/j.php?MTID=m155aa4ab167f13afbfe5809ffedc3306 Meeting number: 2624 873 8506, password: proficiency