Keynote Speakers Abstracts

Prof. Eduard Hovy Prof. Nizar Habash Prof. Tim Baldwin Prof Iryna Gurevych Dr. Sanjay Chawla Dr. Hassan Sawwaf

• Prof. Iryna Gurevych, Full Professor and Head of the UKP Lab, Technical University Darmstadt, (Germany)

Title: Detect – Verify – Communicate: Combating Misinformation with More Realistic NLP

Dealing with misinformation is a grand challenge of the information society directed at equipping computer users with effective tools for identifying and debunking misinformation. Current Natural Language Processing (NLP) including its fact-checking research fails to meet the expectations of real-life scenarios. In this talk, we show why the past work on fact-checking has not yet led to truly useful tools for managing misinformation, and discuss our ongoing work on more realistic solutions. NLP systems are expensive in terms of financial cost, computation, and manpower needed to create data for the learning process. With that in mind, we are pursuing research on detection of emerging misinformation topics to focus human attention on the most harmful, novel examples. Automatic methods for claim verification rely on large, high-quality datasets. To this end, we have constructed two corpora for fact checking, considering larger evidence documents and pushing the state of the art closer to the reality of combating misinformation. We further compare the capabilities of automatic, NLP-based approaches to what human fact checkers actually do, uncovering critical research directions for the future. To edify false beliefs, we are collaborating with cognitive scientists and psychologists to automatically detect and respond to attitudes of vaccine hesitancy, encouraging anti-vaxxers to change their minds with effective communication strategies

Bio: Iryna Gurevych (PhD 2003, U. Duisburg-Essen, Germany) is professor of Computer Science and director of the Ubiquitous Knowledge Processing (UKP) Lab at the Technical University (TU) of Darmstadt in Germany. Her main research interests are in machine learning for large-scale language understanding and text semantics. Iryna’s work has received numerous awards. Examples are the ACL fellow award 2020 and the first Hessian LOEWE Distinguished Chair award (2,5 mil. Euro) in 2021. Iryna is co-director of the NLP program within ELLIS, a European network of excellence in machine learning. She is currently the vice-president of the Association of Computational Linguistics.

• Dr. Hassan Sawwaf, Founder of aiXplain, inc, Los Gatos, California, (USA)

Talk: Benchmarking as a Driver for Innovation

Arabic Speech and Language Processing in the last 20 years has achieved great results that enabled new and exciting applications. To enable further acceleration, we will need to increase collaboration across teams and organizations. At the heart of this is benchmark, which includes alignment over task definitions, metrics and finally diagnostic benchmarks. Benchmarking benefits from the broad engagement of diverse stakeholders (academia, industry and policy makers). Hassan will draw parallels from past projects with DARPA that were the drivers of today's state of NLP, and sketch out a model that will help the Arabic Speech and NLP community to continue raise the bar to drive the state of the art.

Bio: With more than 20 years of expertise in bringing cutting edge technology from academia to market, Hassan Sawaf is well-recognized in the field of applied science and machine learning, with applications in machine translation, speech recognition, computer vision, natural language understanding and dialog, process optimization, and other fields. Hassan was until June 2020 Director, Facebook AI with a focus on building machine learning technology to leverage multimodal signals for comprehension, machine induction and personalization across various applications in various domains, among them applications in the augmented reality and virtual reality (AR/VR) field.

Before that, he was Director, Applied Science and Artificial Intelligence at Amazon Web Services from 2016 to 2019, and was responsible with his team to drive science for existing machine learning services on AWS and incubate new technologies and services in this field for the AWS customers. He holds a Master of Science degree in computer science from Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen with a minor in Human Medicine, and attended the same university for his Ph.D. studies, focusing on Machine Learning and Applied Artificial Intelligence.

Before that, he established and headed the Artificial Intelligence teams in eBay, enabling the business with machine translation, language understanding and processing, computer vision, behavior modeling, dialog processing, and other fields, to improve the user experience for the shopper and seller.

Prior to joining eBay in April 2013, Hassan held management roles with several companies in the language solutions space, including CEO of AIXPLAIN AG, COO of AppTek Inc., CEO of Net2Voice, and Chief Scientist at SAIC.

• Prof. Nizar Habash, Professor of Computer Science New York University Abu Dhabi

Title: Gender Bias in Arabic Machine Translation

Gender bias in natural language processing applications, particularly machine translation, has been receiving increasing attention. For example, the English sentences "I am a doctor" and "I am a nurse" are translated automatically to Arabic as "أنا طبيب" (I am a [male] doctor) and "أنا ممرضة" ( I am a [female] nurse). Much of the research on this issue has focused on mitigating gender bias in English models and systems. In this talk, we discuss the various interlocking sources of bias in machine translation, and specifically consider Arabic and gender. We also present a set of solutions that include a newly developed first-of-its-kind corpus for modeling gender in Arabic, and a system for gender rewriting of machine translation output to match user needs.

Bio: Nizar Habash is a Professor of Computer Science at New York University Abu Dhabi (NYUAD). He is also the director of the Computational Approaches to Modeling Language (CAMeL) Lab. Professor Habash specializes in natural language processing and computational linguistics. Before joining NYUAD in 2014, he was a research scientist at Columbia University's Center for Computational Learning Systems. He received his PhD in Computer Science from the University of Maryland College Park in 2003. He has two bachelors degrees, one in Computer Engineering and one in Linguistics and Languages. His research includes extensive work on machine translation, morphological analysis, and computational modeling of Arabic and its dialects. Professor Habash has been a principal investigator or co-investigator on over 25 research grants. And he has over 200 publications including a book entitled "Introduction to Arabic Natural Language Processing". His website is www.nizarhabash.com.

• Prof. Tim Baldwin, MBZUAI (Associate Provost and Head of NLP Department) and Melbourne Laureate Professor in the School of Computing and Information Systems, The University of Melbourne (Australia)

Title: Fairness in Natural Language Processing

Natural language processing (NLP) has made truly impressive progress in recent years, and is being deployed in an ever-increasing range of user-facing

settings. Accompanied by this progress has been a growing realisation of inequities in the performance of naively-trained NLP models for users of

different demographics, with minorities typically experiencing lower performance levels. In this talk, I will illustrate the nature and magnitude of the problem, and outline a number of approaches that can be used to train fairer models based on different data settings, without sacrificing overall performance levels.

Bio: Tim Baldwin is Associate Provost (Academic and Student Affairs) and Acting Head of the Department of Natural Language Processing, Mohamed bin Zayed

University of Artificial Intelligence in addition to being a Melbourne Laureate Professor in the School of Computing and Information Systems, The

University of Melbourne. His primary research focus is on natural language processing (NLP), including social media analytics, deep learning, and

computational social science. Tim completed a BSc(CS/Maths) and BA(Linguistics/Japanese) at The University of Melbourne in 1995, and an MEng(CS) and PhD(CS) at the Tokyo Institute of Technology in 1998 and 2001, respectively. Prior to joining The University of Melbourne in 2004, he was a Senior Research Engineer at the Center for the Study of Language and Information, Stanford University (2001-2004). His research has been funded by organisations including the Australia Research Council, Google, Microsoft, Xerox, ByteDance, SEEK, NTT, and Fujitsu, and has been featured in MIT Tech Review, IEEE Spectrum, The Times, ABC News, The Age/Sydney Morning Herald, Australian Financial Review, and The Australian. He is the author of well over 400 peer-reviewed publications across diverse topics in natural language processing and AI, with over 18,000 citations and an h-index of 65 (Google Scholar), in addition to being an ARC Future Fellow, and the recipient of a number of best paper awards at top conferences.

• Prof. Eduard Hovy, Language Technology Institute, Carnegie Mellon University (USA)

Title: On Explanation for AI

AI systems of all kinds will increasingly influence human life, including medical diagnosis, self-driving cars, robot manufacturing, job and credit card application decisions, news targeting, etc. When they make mistakes society will want to know who to blame. AI systems must be able to explain their reasoning in terms that people can understand and that system builders can use to change system behavior. At present, the best AI machine learning systems are deep neural networks and large language models whose ‘explanations’ are deeply inadequate, at best. This talk lists desiderata for socially acceptable explanations and describes several attempts to meet them when using deep neural networks.

Bio: Eduard Hovy received the Ph.D. degree in computer science from Yale University. He received the honorary doctorates from the National University of Distance Education (UNED) in Madrid, in 2013, and the University of Antwerp, in 2015. He is currently a Research Professor of the Language Technologies Institute, Carnegie Mellon University. He is one of the original 17 Fellows of the Association for Computational Linguistics. He has published more than 500 research papers. His research focuses on various topics, including aspects of the computational semantics of human language. He is a Fellow of the AAAI. He serves or has served on the editorial boards of several journals, such as the ACM Transactions on Asian Language Information Processing TALIP and Language Resources and Evaluation (LRE).

• Dr. Sanjay Chawla, Research director of Qatar Centre for Artificial Intelligence (QCAI) , Qatar

Title: Big Data: Going Beyond Predictions

Recent successes in AI can be attributed to the fact the supervised learning in static prediction tasks is a solved problem. However, predictions on their own are not sufficient for decision making. Here we show that prescriptive learning provides a more appropriate framework for data-driven decision making. We will give two examples from our own work: reinforcement learning for air cargo management and traffic signal coordination, where tangible progress has been made.

Bio: Sanjay Chawla is the Research Director of the Qatar Center for AI, QCRI. Before joining QCRI, he was a professor in the Faculty of Engineering and IT, University of Sydney. He was PC Co-chair of ACM SIGKDD 2021.