The following speakers have graciously agreed to give keynotes at EMNLP 2020.
Information Extraction Through the Years: How Did We Get Here?
Claire Cardie is the John C. Ford Professor of Engineering in the Departments of Computer Science and Information Science at Cornell University. She has worked since the early 1990's on application of machine learning methods to problems in Natural Language Processing --- on topics ranging from information extraction, noun phrase coreference resolution, text summarization and question answering to the automatic analysis of opinions, argumentation, and deception in text. She has served on the executive committees of the ACL and AAAI and twice as secretary of NAACL. She has been Program Chair for ACL/COLING, EMNLP and CoNLL, and General Chair for ACL in 2018. Cardie was named a Fellow of the ACL in 2015 and a Fellow of the Association for Computing Machinery (ACM) in 2019. At Cornell, she led the development of the university's academic programs in Information Science and was the founding Chair of its Information Science Department.
Friends Don’t Let Friends Deploy Black-Box Models: The Importance of Intelligibility in Machine Learning
Rich Caruana is a Senior Principal Researcher at Microsoft. His focus is on intelligible/transparent modeling, machine learning for medical decision making, deep learning, and computational ecology. Before joining Microsoft, Rich was on the faculty in Computer Science at Cornell, at UCLA's Medical School, and at CMU's Center for Learning and Discovery. Rich's Ph.D. is from CMU. His work on Multitask Learning helped create interest in a subfield of machine learning called Transfer Learning. Rich received an NSF CAREER Award in 2004 (for Meta Clustering), best paper awards in 2005 (with Alex Niculescu-Mizil), 2007 (with Daria Sorokina), and 2014 (with Todd Kulesza, Saleema Amershi, Danyel Fisher, and Denis Charles), and co-chaired KDD in 2007 with Xindong Wu. "
Linguistic Behaviour and the Realistic Testing of NLP Systems.
Janet Pierrehumbert is the Professor of Language Modelling in the Department of Engineering Science at the University of Oxford. She received her BA in Linguistics and Mathematics at Harvard in 1975, and her Ph.D in Linguistics from MIT in 1980. Much of her Ph.D dissertation research on English prosody and intonation was carried out at AT&T Bell Laboratories, where she was also a Member of Technical Staff from 1982 to 1989. After she moved to Northwestern University in1989, her research program used a wide variety of experimental and computational methods to explore how lexical systems emerge in speech communities. She showed that the mental representations of words are at once abstract and phonetically detailed, and that social factors interact with cognitive factors as lexical patterns are learned, remembered, and generalized. Pierrehumbert joined the faculty at the University of Oxford in 2015 as a member of the interdisciplinary Oxford e-Research Centre. Her current research uses machine-learning methods to model the dynamics of on-line language. Her latest project, funded by the UK EPSRC, seeks to develop new NLP methods to characterize exaggeration, cohesion, and fragmentation in on-line forums.
Pierrehumbert is a Fellow of the Linguistic Society of America, the Cognitive Science Society, and the American Academy of Arts and Sciences. She was elected to the National Academy of Sciences in 2019. She is the recipient of the 2020 Medal for Scientific Achievement from the International Speech Communication Association.