​ The following speakers have graciously agreed to give keynotes at EMNLP 2020.

Claire Cardie

Information Extraction Through the Years: How Did We Get Here?

In this talk, I'll examine the state of the NLP subfield of information extraction from its inception almost 30 years ago to its current realization in neural network models. Which aspects of the original formulation of the task are more or less solved? In what ways are current state-of-the-art methods still falling short? What's up next for information extraction?

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.

Rich Caruana

Friends Don’t Let Friends Deploy Black-Box Models: The Importance of Intelligibility in Machine Learning

In machine learning sometimes tradeoffs must be made between accuracy and intelligibility: the most accurate models usually are not very intelligible, and the most intelligible models usually are less accurate. This can limit the accuracy of models that can safely be deployed in mission-critical applications where being able to understand, validate, edit, and ultimately trust a model is important. We have been working on a learning method to escape this tradeoff that is as accurate as full complexity models such as boosted trees and random forests, but more intelligible than linear models. This makes it easy to understand what the model has learned and to edit the model when it learns inappropriate things. Making it possible for humans to understand and repair a model is critical because most training data has unexpected problems. I’ll present several case studies where these high-accuracy GAMs discover surprising patterns in the data that would have made deploying a black-box model inappropriate. I’ll also show how these models can be used to detect and correct bias. And if there’s time, I’ll briefly discuss using intelligible GAM models to predict COVID-19 mortality.

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. "

Janet Pierrehumbert

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.