Embedded Files
Dr. Hang Li is a director of AI Lab, Bytedance Technology (also known as Toutiao). He is an ACL Fellow, an IEEE Fellow and an ACM Distinguished Scientist. His research areas include natural language processing, information retrieval, machine learning, and data mining. Hang graduated from Kyoto University in 1988 and earned his PhD from the University of Tokyo in 1998. He worked at NEC Research as researcher from 1990 to 2001, Microsoft Research Asia as senior researcher and research manager from 2001 to 2012, and chief scientist and director of Huawei Noah’s Ark Lab from 2012 to 2017. He joined Bytedance in 2017.
Title: Deep Learning for Natural Language Processing: Current and Future
Abstract:
Why is deep learning so powerful for natural language processing? How will deep learning for natural language processing evolve in the future? These are important questions which many researchers in the field may be thinking of. In this talk, I will provide my answers to the questions. First, I will summarize the advantages and disadvantages of deep learning, as well as the major problems of natural language processing. Next, I will discuss the reason behind the success of deep learning in natural language processing, particularly search and recommendation. Finally, I will share my view on future directions of deep learning for natural language processing.
Prof. Qiaozhu Mei is a professor in the School of Information and the Department of EECS at the University of Michigan. His research focuses on large-scale data mining, machine learning, information retrieval, and natural language processing, with broad applications to social media, Web, and health informatics. Qiaozhu is an ACM distinguished member (2017) and a recipient of the NSF Career Award (2011). His work has received multiple best paper awards at ICML, KDD, WWW, WSDM, and other major conferences in computing. He has served as the General Co-Chair of SIGIR 2018. He is the founding director of the master degree of applied data science at the University of Michigan.
Title: Explanatory Natural Language Processing: Formulation, Methods, and Evaluation.
Abstract: Building explanatory models and applications is widely considered as a critical component towards the fairness, accountability, and transparency of machine learning. In practice, however, the necessity and the practical value of explaining a black-box neural network model are still under debate. This controversy is largely due to the lack of a clean formulation and objective evaluation about the explainability of a model. In this talk, I will introduce a novel conceptual formulation of explanatory machine learning, which centers on how human users make joint decisions with a machine learning based predictor. This general framework also leads to a natural and robust way of evaluating the explanations, through which the goodness of an explanatory model is measured by how much better the joint decisions are with vs. without the explanations. I will introduce our recent work along this direction, including an end-to-end adversarial attention network for explanatory natural language processing, its application to identifying toxicity from social media posts, and user studies at scale to evaluate its effectiveness in practice.
Invited Talk
Invited Talk
Title: Democratize conversational user interface
Abstract: Chatbot is essentially an application that allows users to interact with your business through a conversational interface. Due to its lower learning curve, and the ability to directly access information and services, chatbots are widely predicted to be the next-generation user interface and hence attracted attention from both research and industry. For example, two years in a row, Dialogue is the dominant topic at ACL. But despite the interest and effort level on chatbot, in reality, the good conversational user experience is still far and between. In this talk, we will analyze some common misconceptions about building conversational interface, hypothesize why building GUI app is so much cheaper/easier than building their CUI counterpart and discuss ways that are needed to make the conversational user interface as ubiquitous as everyday apps on your phone.
Page updated
Google Sites
Report abuse