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BIOGRAPHY: Prof. David Yarowsky is a Professor of Computer Science at Johns Hopkins University and a 25-year faculty member in its Center for Language and Speech Processing. His research interests span many areas of massively multilingual natural language processing, with a particular focus on very low-resource machine translation, computational morphology, cross-language knowledge transfer, lexicon induction, lexical semantics, computational etymology, word sense disambiguation and minimally supervised machine learning. He has served in many leadership roles in the Association for Computational Linguistics, including as co-founder of SIGDAT and EMNLP, ACL executive committee member, multiple general and program chairs, Computational Linguistics editorial board member and ACL treasurer. Dr. Yarowsky is an NSF CAREER Award and Michael C. Rockefeller Fellowship recipient, summa cum laude graduate of Harvard University, ACL Test-of-Time award winner, and is a Fellow of the Association for Computational Linguistics.
ABSTRACT: People love to argue. In recent years, Artificial Intelligence has achieved great advances in modelling natural language argumentation. While analysing and creating arguments is a highly complex (and enjoyable!) task at which even humans are not good, let alone perfect, we describe our natural language processing (NLP) research to identify arguments, their stance and aspects, aggregate arguments into topically coherent clusters, and finally, even to generate new arguments, given their desired topic, aspect and stance. The talk will tell you the story how the ArgumenText project has been conceptualized into a set of novel NLP tasks and highlight their main research outcomes. Argument mining has a tremendous number of possible applications, of which the talk discusses a few selected ones.
BIOGRAPHY: Prof. Iryna Gurevych (PhD 2003, U. Duisburg-Essen, Germany) is a professor of Computer Science and director of the Ubiquitous Knowledge Processing (UKP) Lab at the Technical University (TU) of Darmstadt in Germany. She joined TU Darmstadt in 2005 (tenured as full professor in 2009). Her main research interest is machine learning for large-scale language understanding, including text analysis for social sciences and humanities. She is one of the co-founders of the field of computational argumentation with many applications, such as the identification of fake news and decision-making support. Iryna’s work received numerous awards, e.g. a highly competitive Lichtenberg-Professorship Award from the Volkswagen Foundation and a DFG Emmy-Noether Young Researcher’s Excellence Career Award. Iryna was elected to be President of SIGDAT, one of the most important scientific bodies in the ACL community. She was program co-chair of ACL’s most important conference in 2018, the Annual Meeting of the Association for Computational Linguistics, and she is General Chair of *SEM 2020, the 9th Joint Conference on Lexical and Computational Semantics. In 2020, Iryna has been elected as the ACL VP-elect and selected as an ACL Fellow.
ABSTRACT: Recent automated QA system achieve some strong results using a variety of techniques.
How do complex/deep/neural QA approaches differ from simple/shallow ones? In early
QA, pattern-learning and -matching techniques identified the appropriate factoid answer(s).
In deep QA, neural architectures learn and apply more-flexible generalized word/type-
sequence ‘patterns’. However, many QA tasks require some sort of intermediate reasoning
or other inference procedures that go beyond generalized patterns of words and phrases.
One approach focuses on learning small access functions to locate the answer in structured
resources like tables or databases. But much (or most) online information is not structured,
and what to do in this case is unclear. Most current ‘deep’ QA research takes a one-size-fits-
all approach based on the hope that a multi-layer neural architecture will somehow learn to
encode inference steps automatically. The main problem facing this approach is the
difficulty in determining exactly what reasoning is required, and what knowledge resources
are needed in support. How should the QA community address this challenge? In this talk I
outline the problem, define four levels of QA, and propose a general direction for future
BIOGRAPHY: Prof. Eduard Hovy is a research professor at the Language Technologies Institute in the School of Computer Science at Carnegie Mellon University. He also holds adjunct professorships in CMU’s Machine Learning Department and at USC (Los Angeles). Dr. Hovy completed a Ph.D. in Computer Science (Artificial Intelligence) at Yale University in 1987 and was awarded honorary doctorates from the National Distance Education University (UNED) in Madrid in 2013 and the University of Antwerp in 2015. He is one of the initial 17 Fellows of the Association for Computational Linguistics (ACL) and is also a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI). Dr. Hovy’s research focuses on computational semantics of language, and addresses various areas in Natural Language Processing and Data Analytics, including in-depth machine reading of text, information extraction, automated text summarization, question answering, the semi-automated construction of large lexicons and ontologies, and machine translation. In late 2020 his Google h-index was 89, with over 42,000 citations. Dr. Hovy is the author or co-editor of eight books and over 450 technical articles and is a popular invited speaker. From 2003 to 2015 he was co-Director of Research for the Department of Homeland Security’s Center of Excellence for Command, Control, and Interoperability Data Analytics, a distributed cooperation of 17 universities. In 2001 Dr. Hovy served as President of the international Association of Computational Linguistics (ACL), in 2001–03 as President of the International Association of Machine Translation (IAMT), and in 2010–11 as President of the Digital Government Society (DGS). Dr. Hovy regularly co-teaches Ph.D.-level courses and has served on Advisory and Review Boards for both research institutes and funding organizations in Germany, Italy, Netherlands, Ireland, Singapore, and the USA.