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
research.
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.