Monthly Archives: December 2021

Prof. Iryna Gurevych (Technical University of Darmstadt, Germany)

Short bio

Iryna Gurevych is a German computer scientist. She is Professor at the Department of Computer Science of the Technical University of Darmstadt and Director of Ubiquitous Knowledge Processing Lab. She has a strong background in information extraction, semantic text processing, machine learning and innovative applications of NLP to social sciences and humanities.

Iryna Gurevych has published over 300 publications in international conferences and journals and is member of programme and conference committees of more than 50 high-level conferences and workshops (ACL, EACL, NAACL, etc.).  She is the holder of several awards, including the Lichtenberg-Professorship Career Award und the Emmy-Noether Career Award (both in 2007). In 2021 she received the first LOEWE-professorship of the LOEWE programme. She has been selected as a ACL Fellow 2020 for her outstanding work in natural language processing and machine learning and is the Vice-president-elect of the ACL since 2021.

Talk Abstract

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

Dealing with misinformation is a grand challenge of the information society directed at equipping the 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.

Prof. Shuly Wintner (University of Haifa, Israel)

Short bio

Shuly Wintner is professor of computer science at the University of Haifa, Israel. His research spans across various areas of computational linguistics and natural language processing, including formal grammars, morphology, syntax, language resources, translation, and multilingualism.

He served as the editor-in-chief of Springer’s Research on Language and Computation, a program co-chair of EACL-2006, and the general chair of EACL-2014. He was among the founders, and twice (6 years) the chair, of ACL SIG Semitic. He is currently the Chair of the EACL.


 

Talk abstract

The Hebrew Essay Corpus

The Hebrew Essay Corpus is an annotated corpus of Hebrew language argumentative essays authored by prospective higher-education students. The corpus includes both essays by native speakers, written as part of the psychometric exam that is used to assess their future success in academic studies; and essays authored by non-native speakers, with three different native languages, that were written as part of a language aptitude test. The corpus is uniformly encoded and stored. The non-native essays were annotated with target hypotheses whose main goal is to make the texts amenable to automatic processing (morphological and syntactic analysis).

I will describe the corpus and the error correction and annotation schemes used in its analysis. In addition, I will discuss some of the challenges involved in identifying and analyzing non-native language use in general, and propose various ways for dealing with these challenges. Then, I will present classifiers that can accurately distinguish between native and non-native authors; determine the mother tongue of the non-natives; and predict the proficiency level of non-native Hebrew learners. This is important for practical (mainly educational) applications, but the endeavor also sheds light on the features that support the classification, thereby improving our understanding of learner language in general, and transfer effects from Arabic, French, and Russian on nonnative Hebrew in particular.

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