We have envisioned that computers will understand natural language and predict what we need help in order to complete tasks via conversational interactions. This talk focuses on context-aware understanding in different levels: 1) word-level contexts in sentences 2) sentence-level contexts in dialogues. Word-level contexts contribute both semantic and syntactic relations, which benefit sense representation learning and knowledge-guided language understanding. Also, sentence-level contexts may significantly affect dialogue-level performance. This talk investigates how misunderstanding of a single-turn utterance degrades the success rate of an end-to-end reinforcement learning based dialogue system. Then we will highlight challenges and recent trends driven by deep learning and intelligent assistants.
Yun-Nung (Vivian) Chen is an assistant professor in the Department of Computer Science and Information Engineering at National Taiwan University. Her research interests include language understanding, dialogue systems, natural language processing, deep learning, and multimodality. She received the Google Faulty Research Awards 2016, two Best Student Paper Awards from IEEE ASRU 2013 and IEEE SLT 2010 and a Student Best Paper Nominee from INTERSPEECH 2012. Chen earned the Ph.D. degree from School of Computer Science at Carnegie Mellon University, Pittsburgh in 2015. Prior to joining National Taiwan University, she worked for Microsoft Research in the Deep Learning Technology Center. (http://vivianchen.idv.tw)