Yevgeni Berzak

VLDB 2026 Research / reviewers in the wild / expert

dblp:144/7532 · DBLP profile ↗

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25ranked-venue papers

9as first author

12since 2021 · last 2026

0000-0003-4474-1727ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 24 · 9 first-author · 11 since 2021Applied, interdisciplinary, general and emerging computing · 4 · 1 first-author · 4 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

Year	Publication	Venue	Position
2026	The MultiplEYE Text Corpus: Towards a Diverse and Ever-Expanding Multilingual Text Corpus Ramune Kaspere, Anna Bondar, Sergiu Nisioi, Maja Stegenwallner-Schütz, Hanne B. Søndergaard Knudsen, Ana Matic Skoric, Eva Pavlinusic Vilus, Dorota Klimek-Jankowska, Chiara Tschirner, Not Battesta Soliva, Deborah N. Jakobi, Cui Ding, Dima Abu Romi, Cengiz Acartürk, Matilda Agdler, Anton Marius Alexandru, Mohd Faizan Ansari, Annalisa Arcidiacono, Elizabete Ausma Velta Barisa, Ana Bautista, Lisa Beinborn, Yevgeni Berzak, Nedeljka Bjelanovic, Anna Isabelle Bothmann, Jan Brasser, Caterina Cacioli, Anila Çepani, Ilze Ceple, Adelina Çerpja, Dalí Chirino, Jan Chromý, Alessandro Corona Mendozza, Iria de-Dios-Flores, Nazik Dinçtopal Deniz, Ana Dosen, Kristian Elersic, Inmaculada Fajardo, Zigmunds Freibergs, Angelina Ganebnaya, Jessica Gomes, Annjo Klungervik Greenall, Alba Haveriku, Anamaria Hodivoianu, Yu-Yin Hsu, Amanda Isaksen, Andreia Janeiro, Kristine M. Jensen de López, Aleksandar Jevremovic, Vojislav Jovanovic, Hanna Kedzierska, Nik Kharlamov, Sara Kosutar, Nelda Kote, Vanja Kovic, Izabela Krejtz, Thyra Krosness, Oleksandra Kuvshynova, Eilam Lavy, Ella Lion, Marta Lockiewicz, Kaidi Lõo, Paula Luegi, Mircea Mihai Marin, Clara Martin, Svitlana Matvieieva, Diane C. Mézière, Xavier Mínguez-López, Valeriia Modina, Jurgita Motiejuniene, Marie-Luise Müller, Tolgonai Nasipbek kyzy, Jamal Abdul Nasir, Johanne Sofie Krog Nedergård, Aysegül Özkan, Patrizia Paggio, Marijan Palmovic, Maria Christina Panagiotopoulou, Alberto Parola, Helena Pérez, Klaudia Petersen, Anja Podlesek, Eva Pospísilová, Marta Praulina, Mikulás Preininger, Loredana Punga, Diego Rossini, Spela Rot, Habib Sani Yahaya, Irina A. Sekerina, Anne Gabija Skadina, Jordi Solé i Casals, Lonneke van der Plas, Saara M. Varjopuro, Spyridoula Varlokosta, João Veríssimo, Oskari Juhapekka Virtanen, Nemanja Vracar, Mila Dimitrova-Vulchanova, Ahmad Mustapha Wali, Peizheng Wu, Nilgün Yücel, Stefan Frank, Nora Hollenstein, Lena A. Jäger, Somayeh Bakhtiari	LREC	22
2025	Déjà Vu? Decoding Repeated Reading from Eye Movements abstract Yoav Meiri, Omer Shubi, Cfir Avraham Hadar, Ariel Kreisberg Nitzav, Yevgeni Berzak. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025. Yoav Meiri, Omer Shubi, Cfir Avraham Hadar, Ariel Kreisberg Nitzav, Yevgeni Berzak	ACL (1)	5
2025	Decoding Reading Goals from Eye Movements abstract Readers can have different goals with respect to the text that they are reading.Can these goals be decoded from their eye movements over the text?In this work, we examine for the first time whether it is possible to distinguish between two types of common reading goals: information seeking and ordinary reading for comprehension.Using large-scale eye tracking data, we address this task with a wide range of models that cover different architectural and data representation strategies, and further introduce a new model ensemble.We find that transformer-based models with scanpath representations coupled with language modeling solve it most successfully, and that accurate predictions can be made in real time, shortly after the participant started reading the text.We further introduce a new method for model performance analysis based on mixed effect modeling.Combining this method with rich textual annotations reveals key properties of textual items and participants that contribute to the difficulty of the task, and improves our understanding of the variability in eye movement patterns across the two reading regimes. 1 Omer Shubi, Cfir Avraham Hadar, Yevgeni Berzak	ACL (1)	3
2025	The Effect of Text Simplification on Reading Fluency and Reading Comprehension in L1 English Speakers Keren Gruteke Klein, Omer Shubi, Shachar Frenkel, Yevgeni Berzak	CogSci	4
2025	MultiplEYE: Creating a multilingual eye-tracking-while-reading corpus abstract Contains fulltext : 326363.pdf (Publisher’s version ) (Open Access) Deborah N. Jakobi, Maja Stegenwallner-Schütz, Nora Hollenstein, Cui Ding, Ramune Kaspere, Ana Matic Skoric, Eva Pavlinusic Vilus, Stefan Frank, Marie-Luise Müller, Kristine M. Jensen de López, Nik Kharlamov, Hanne B. Søndergaard Knudsen, Yevgeni Berzak, Ella Lion, Irina A. Sekerina, Cengiz Acartürk, Mohd Faizan Ansari, Katarzyna Harezlak, Pawel Kasprowski, Ana Bautista, Lisa Beinborn, Anna Bondar, Antonia Boznou, Leah Bradshaw, Jana Mara Hofmann, Thyra Krosness, Not Battesta Soliva, Anila Çepani, Kristina Cergol, Ana Dosen, Marijan Palmovic, Adelina Çerpja, Dalí Chirino, Jan Chromý, Vera Demberg, Iza Skrjanec, Nazik Dinçtopal Deniz, Inmaculada Fajardo, Mariola Giménez-Salvador, Xavier Mínguez-López, Maros Filip, Zigmunds Freibergs, Jessica Gomes, Andreia Janeiro, Paula Luegi, João Veríssimo, Sasho Gramatikov, Jana Hasenäcker, Alba Haveriku, Nelda Kote, Muhammad Mohsin Kamal, Hanna Kedzierska, Dorota Klimek-Jankowska, Sara Kosutar, Daniel Krakowczyk, Izabela Krejtz, Marta Lockiewicz, Kaidi Lõo, Jurgita Motiejuniene, Jamal Abdul Nasir, Johanne Sofie Krog Nedergård, Aysegül Özkan, Mikulás Preininger, Loredana Punga, David R. Reich, Chiara Tschirner, Spela Rot, Andreas Säuberli, Jordi Solé i Casals, Ekaterina Strati, Igor Svoboda, Evis Trandafili, Spyridoula Varlokosta, Mila Dimitrova-Vulchanova, Lena A. Jäger	ETRA	13
2025	EyeBench: Predictive Modeling from Eye Movements in Reading abstract We present EyeBench, the first benchmark designed to evaluate machine learning models that decode cognitive and linguistic information from eye movements during reading. EyeBench offers an accessible entry point to the challenging and underexplored domain of modeling eye tracking data paired with text, aiming to foster innovation at the intersection of multimodal AI and cognitive science. The benchmark provides a standardized evaluation framework for predictive models, covering a diverse set of datasets and tasks, ranging from assessment of reading comprehension to detection of developmental dyslexia. Progress on the EyeBench challenge will pave the way for both practical real-world applications, such as adaptive user interfaces and personalized education, and scientific advances in understanding human language processing. The benchmark is released as an open-source software package which includes data downloading and harmonization scripts, baselines and state-of-the-art models, as well as evaluation code, publicly available at https://github.com/EyeBench/eyebench. Omer Shubi, David R. Reich, Keren Gruteke Klein, Yuval Angel, Paul Prasse, Lena A. Jäger, Yevgeni Berzak	NeurIPS	7
2024	Déjà Vu: Eye Movements in Repeated Reading Yoav Meiri, Yevgeni Berzak	CogSci	2
2024	Fine-Grained Prediction of Reading Comprehension from Eye Movements abstract Can human reading comprehension be assessed from eye movements in reading?In this work, we address this longstanding question using large-scale eyetracking data.We focus on a cardinal and largely unaddressed variant of this question: predicting reading comprehension of a single participant for a single question from their eye movements over a single paragraph.We tackle this task using a battery of recent models from the literature, and three new multimodal language models.We evaluate the models in two different reading regimes: ordinary reading and information seeking, and examine their generalization to new textual items, new participants, and the combination of both.The evaluations suggest that the task is highly challenging, and highlight the importance of benchmarking against a strong text-only baseline.While in some cases eye movements provide improvements over such a baseline, they tend to be small.This could be due to limitations of current modelling approaches, limitations of the data, or because eye movement behavior does not sufficiently pertain to finegrained aspects of reading comprehension processes.Our study provides an infrastructure for making further progress on this question.1 Omer Shubi, Yoav Meiri, Cfir Avraham Hadar, Yevgeni Berzak	EMNLP	4
2023	Eye Movements in Information-Seeking Reading Omer Shubi, Yevgeni Berzak	CogSci	2
2022	The Aligned Multimodal Movie Treebank: An audio, video, dependency-parse treebank abstract Adam Yaari, Jan DeWitt, Henry Hu, Bennett Stankovits, Sue Felshin, Yevgeni Berzak, Helena Aparicio, Boris Katz, Ignacio Cases, Andrei Barbu. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022. Adam Uri Yaari, Jan DeWitt, Henry Hu, Bennett Stankovits, Sue Felshin, Yevgeni Berzak, Helena Aparicio, Boris Katz, Ignacio Cases, Andrei Barbu	EMNLP	6
2021	Eye Movement Traces of Linguistic Knowledge Yevgeni Berzak, Roger Levy	CogSci	1
2021	Predicting Text Readability from Scrolling Interactions abstract Judging the readability of text has many important applications, for instance when performing text simplification or when sourcing reading material for language learners.In this paper, we present a 518 participant study which investigates how scrolling behaviour relates to the readability of English texts.We make our dataset publicly available and show that (1) there are statistically significant differences in the way readers interact with text depending on the text level, (2) such measures can be used to predict the readability of text, and (3) the background of a reader impacts their reading interactions and the factors contributing to text difficulty. 1 Sian Gooding, Yevgeni Berzak, Tony Mak, Matthew Sharifi	CoNLL	2
2020	STARC: Structured Annotations for Reading Comprehension abstract We present STARC (Structured Annotations for Reading Comprehension), a new annotation framework for assessing reading comprehension with multiple choice questions.Our framework introduces a principled structure for the answer choices and ties them to textual span annotations.The framework is implemented in OneStopQA, a new high-quality dataset for evaluation and analysis of reading comprehension in English.We use this dataset to demonstrate that STARC can be leveraged for a key new application for the development of SAT-like reading comprehension materials: automatic annotation quality probing via span ablation experiments.We further show that it enables in-depth analyses and comparisons between machine and human reading comprehension behavior, including error distributions and guessing ability.Our experiments also reveal that the standard multiple choice dataset in NLP, RACE (Lai et al., 2017), is limited in its ability to measure reading comprehension.47% of its questions can be guessed by machines without accessing the passage, and 18% are unanimously judged by humans as not having a unique correct answer.OneStopQA provides an alternative test set for reading comprehension which alleviates these shortcomings and has a substantially higher human ceiling performance.1 Yevgeni Berzak, Jonathan Malmaud, Roger Levy	ACL	1
2020	Classifying Syntactic Errors in Learner Language abstract We present a method for classifying syntactic errors in learner language, namely errors whose correction alters the morphosyntactic structure of a sentence.The methodology builds on the established Universal Dependencies syntactic representation scheme, and provides complementary information to other error-classification systems.Unlike existing error classification methods, our method is applicable across languages, which we showcase by producing a detailed picture of syntactic errors in learner English and learner Russian.We further demonstrate the utility of the methodology for analyzing the outputs of leading Grammatical Error Correction (GEC) systems.* First two authors contributed equally. 1 Code can be found in github repo GEC UD divergences.Matrices directly mentioned are included in the appendix. Leshem Choshen, Dmitry Nikolaev 0002, Yevgeni Berzak, Omri Abend	CoNLL	3
2020	Bridging Information-Seeking Human Gaze and Machine Reading Comprehension abstract In this work, we analyze how human gaze during reading comprehension is conditioned on the given reading comprehension question, and whether this signal can be beneficial for machine reading comprehension.To this end, we collect a new eye-tracking dataset with a large number of participants engaging in a multiple choice reading comprehension task.Our analysis of this data reveals increased fixation times over parts of the text that are most relevant for answering the question.Motivated by this finding, we propose making automated reading comprehension more human-like by mimicking human information-seeking reading behavior during reading comprehension.We demonstrate that this approach leads to performance gains on multiple choice question answering in English for a state-of-the-art reading comprehension model. Jonathan Malmaud, Roger Levy, Yevgeni Berzak	CoNLL	3
2019	Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing abstract Linguistic typology aims to capture structural and semantic variation across the world’s languages. A large-scale typology could provide excellent guidance for multilingual Natural Language Processing (NLP), particularly for languages that suffer from the lack of human labeled resources. We present an extensive literature survey on the use of typological information in the development of NLP techniques. Our survey demonstrates that to date, the use of information in existing typological databases has resulted in consistent but modest improvements in system performance. We show that this is due to both intrinsic limitations of databases (in terms of coverage and feature granularity) and under-utilization of the typological features included in them. We advocate for a new approach that adapts the broad and discrete nature of typological categories to the contextual and continuous nature of machine learning algorithms used in contemporary NLP. In particular, we suggest that such an approach could be facilitated by recent developments in data-driven induction of typological knowledge. Edoardo Maria Ponti, Helen O'Horan, Yevgeni Berzak, Ivan Vulic, Roi Reichart, Thierry Poibeau, Ekaterina Shutova, Anna Korhonen	Comput. Linguistics	3
2018	Grounding language acquisition by training semantic parsers using captioned videos abstract We develop a semantic parser that is trained in a grounded setting using pairs of videos captioned with sentences. This setting is both data-efficient, requiring little annotation, and similar to the experience of children where they observe their environment and listen to speakers. The semantic parser recovers the meaning of English sentences despite not having access to any annotated sentences. It does so despite the ambiguity inherent in vision where a sentence may refer to any combination of objects, object properties, relations or actions taken by any agent in a video. For this task, we collected a new dataset for grounded language acquisition. Learning a grounded semantic parser — turning sentences into logical forms using captioned videos — can significantly expand the range of data that parsers can be trained on, lower the effort of training a semantic parser, and ultimately lead to a better understanding of child language acquisition. Candace Ross, Andrei Barbu, Yevgeni Berzak, Battushig Myanganbayar, Boris Katz	EMNLP	3
2018	Assessing Language Proficiency from Eye Movements in Reading abstract Yevgeni Berzak, Boris Katz, Roger Levy. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018. Yevgeni Berzak, Boris Katz, Roger Levy	NAACL-HLT	1
2017	Predicting Native Language from Gaze abstract A fundamental question in language learning concerns the role of a speaker's first language in second language acquisition.We present a novel methodology for studying this question: analysis of eye-movement patterns in second language reading of free-form text.Using this methodology, we demonstrate for the first time that the native language of English learners can be predicted from their gaze fixations when reading English.We provide analysis of classifier uncertainty and learned features, which indicates that differences in English reading are likely to be rooted in linguistic divergences across native languages.The presented framework complements production studies and offers new ground for advancing research on multilingualism. 1 Yevgeni Berzak, Chie Nakamura, Suzanne Flynn, Boris Katz	ACL (1)	1
2016	Universal Dependencies for Learner English abstract Yevgeni Berzak, Jessica Kenney, Carolyn Spadine, Jing Xian Wang, Lucia Lam, Keiko Sophie Mori, Sebastian Garza, Boris Katz. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016. Yevgeni Berzak, Jessica Kenney, Carolyn Spadine, Jing Xian Wang, Lucia Lam, Keiko Sophie Mori, Sebastian Garza, Boris Katz	ACL (1)	1
2016	Survey on the Use of Typological Information in Natural Language Processing abstract In recent years linguistic typologies, which classify the world’s languages according to their functional and structural properties, have been widely used to support multilingual NLP. While the growing importance of typologies in supporting multilingual tasks has been recognised, no systematic survey of existing typological resources and their use in NLP has been published. This paper provides such a survey as well as discussion which we hope will both inform and inspire future work in the area. Helen O'Horan, Yevgeni Berzak, Ivan Vulic, Roi Reichart, Anna Korhonen	COLING	2
2016	Anchoring and Agreement in Syntactic Annotations abstract We present a study on two key characteristics of human syntactic annotations: anchoring and agreement. Anchoring is a well known cognitive bias in human decision making, where judgments are drawn towards pre-existing values. We study the influence of anchoring on a standard approach to creation of syntactic resources where syntactic annotations are obtained via human editing of tagger and parser output. Our experiments demonstrate a clear anchoring effect and reveal unwanted consequences, including overestimation of parsing performance and lower quality of annotations in comparison with human-based annotations. Using sentences from the Penn Treebank WSJ, we also report systematically obtained inter-annotator agreement estimates for English dependency parsing. Our agreement results control for parser bias, and are consequential in that they are on par with state of the art parsing performance for English newswire. We discuss the impact of our findings on strategies for future annotation efforts and parser evaluations. Yevgeni Berzak, Yan Huang 0014, Andrei Barbu, Anna Korhonen, Boris Katz	EMNLP	1
2015	Contrastive Analysis with Predictive Power: Typology Driven Estimation of Grammatical Error Distributions in ESL abstract This work examines the impact of crosslinguistic transfer on grammatical errors in English as Second Language (ESL) texts.Using a computational framework that formalizes the theory of Contrastive Analysis (CA), we demonstrate that language specific error distributions in ESL writing can be predicted from the typological properties of the native language and their relation to the typology of English.Our typology driven model enables to obtain accurate estimates of such distributions without access to any ESL data for the target languages.Furthermore, we present a strategy for adjusting our method to low-resource languages that lack typological documentation using a bootstrapping approach which approximates native language typology from ESL texts.Finally, we show that our framework is instrumental for linguistic inquiry seeking to identify first language factors that contribute to a wide range of difficulties in second language acquisition. Yevgeni Berzak, Roi Reichart, Boris Katz	CoNLL	1
2015	Do You See What I Mean? Visual Resolution of Linguistic Ambiguities abstract Understanding language goes hand in hand with the ability to integrate complex contextual information obtained via perception.In this work, we present a novel task for grounded language understanding: disambiguating a sentence given a visual scene which depicts one of the possible interpretations of that sentence.To this end, we introduce a new multimodal corpus containing ambiguous sentences, representing a wide range of syntactic, semantic and discourse ambiguities, coupled with videos that visualize the different interpretations for each sentence.We address this task by extending a vision model which determines if a sentence is depicted by a video.We demonstrate how such a model can be adjusted to recognize different interpretations of the same underlying sentence, allowing to disambiguate sentences in a unified fashion across the different ambiguity types. Yevgeni Berzak, Andrei Barbu, Daniel Harari, Boris Katz, Shimon Ullman	EMNLP	1
2014	Reconstructing Native Language Typology from Foreign Language Usage abstract This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF - 1231216. Yevgeni Berzak, Roi Reichart, Boris Katz	CoNLL	1