Demo Data from VLDB 2026 / reviewers in the wild / expert
Paula Czarnowska
dblp:243/1769
· DBLP profile ↗
5ranked-venue papers
3as first author
3since 2021 · last 2024
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 5 · 3 first-author · 3 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
2 papers |
Language models and text generation · 48% Trustworthy machine learning · 32% Machine translation · 16% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Language models and text generation › trustworthy language model
large language model reliability |
0.8 | 1 | 2024 | Factual Confidence of LLMs: on Reliability and Robustness of Current Estimators · ACL (1) 2024 |
Machine learning › Trustworthy machine learning
uncertainty estimation |
0.8 | 1 | 2024 | Factual Confidence of LLMs: on Reliability and Robustness of Current Estimators · ACL (1) 2024 |
Natural language and speech › Machine translation
bilingual lexicon induction |
0.4 | 1 | 2019 | Don't Forget the Long Tail! A Comprehensive Analysis of Morphological Generalization in Bilingual Lexicon Induction · EMNLP/IJCNLP (1) 2019 |
Natural language and speech › Language models and text generation › linguistic generalization
morphological generalization |
0.4 | 1 | 2019 | Don't Forget the Long Tail! A Comprehensive Analysis of Morphological Generalization in Bilingual Lexicon Induction · EMNLP/IJCNLP (1) 2019 |
Natural language and speech › Information extraction and text analysis
morphological analysis |
0.1 | 1 | 2019 | Don't Forget the Long Tail! A Comprehensive Analysis of Morphological Generalization in Bilingual Lexicon Induction · EMNLP/IJCNLP (1) 2019 |
Methods — techniques the papers use, named apart from their topics
estimator evaluation · 0.8evaluation · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Factual Confidence of LLMs: on Reliability and Robustness of Current EstimatorsabstractMatéo Mahaut, Laura Aina, Paula Czarnowska, Momchil Hardalov, Thomas Müller, Lluis Marquez. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024. Matéo Mahaut, Laura Aina, Paula Czarnowska, Momchil Hardalov, Lluís Màrquez |
ACL (1) | 3 |
| 2022 | UniMorph 4.0: Universal MorphologyabstractThe Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation, and a type-level resource of annotated data in diverse languages realizing that schema. This paper presents the expansions and improvements on several fronts that were made in the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 66 new languages, including 24 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g., missing gender and macrons information. We have amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive. In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet. Khuyagbaatar Batsuren, Omer Goldman, Salam Khalifa, Nizar Habash, Witold Kieras, Gábor Bella, Brian Leonard, Garrett Nicolai, Kyle Gorman, Yustinus Ghanggo Ate, Maria Ryskina, Sabrina J. Mielke, Elena Budianskaya, Charbel El-Khaissi, Tiago Pimentel, Michael Gasser, William Lane 0002, Mohit Raj, Matt Coler, Jaime Rafael Montoya Samame, Delio Siticonatzi Camaiteri, Esaú Zumaeta Rojas, Didier López Francis, Arturo Oncevay, Juan López Bautista, Gema Celeste Silva Villegas, Lucas Torroba Hennigen, Adam Ek, David Guriel, Peter Dirix, Jean-Philippe Bernardy, Andrey Scherbakov, Aziyana Bayyr-ool, Antonios Anastasopoulos, Roberto Zariquiey, Karina Sheifer, Sofya Ganieva, Hilaria Cruz, Ritván Karahóga, Stella Markantonatou, George Pavlidis, Matvey Plugaryov, Elena Klyachko, Ali Salehi, Candy Angulo, Jatayu Baxi, Andrew Krizhanovsky, Natalia Krizhanovskaya, Elizabeth Salesky, Clara Vania, Sardana Ivanova, Jennifer C. White, Rowan Hall Maudslay, Josef Valvoda, Ran Zmigrod, Paula Czarnowska, Irene Nikkarinen, Aelita Salchak, Brijesh Bhatt, Christopher Straughn, Zoey Liu, Jonathan Washington, Yuval Pinter, Duygu Ataman, Marcin Wolinski, Totok Suhardijanto, Anna Yablonskaya, Niklas Stoehr, Hossep Dolatian, Zahroh Nuriah, Shyam Ratan, Francis M. Tyers, Edoardo Maria Ponti, Grant Aiton, Aryaman Arora, Richard J. Hatcher, Ritesh Kumar 0002, Jeremiah Young, Daria Rodionova, Anastasia Yemelina, Taras Andrushko, Igor Marchenko, Polina Mashkovtseva, Alexandra Serova, Emily Tucker Prud'hommeaux, Maria Nepomniashchaya, Fausto Giunchiglia, Eleanor Chodroff, Mans Hulden, Miikka Silfverberg, Arya McCarthy, David Yarowsky, Ryan Cotterell, Reut Tsarfaty, Ekaterina Vylomova |
LREC | 56 |
| 2021 | Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness MetricsabstractAbstract Measuring bias is key for better understanding and addressing unfairness in NLP/ML models. This is often done via fairness metrics, which quantify the differences in a model’s behaviour across a range of demographic groups. In this work, we shed more light on the differences and similarities between the fairness metrics used in NLP. First, we unify a broad range of existing metrics under three generalized fairness metrics, revealing the connections between them. Next, we carry out an extensive empirical comparison of existing metrics and demonstrate that the observed differences in bias measurement can be systematically explained via differences in parameter choices for our generalized metrics. Paula Czarnowska, Yogarshi Vyas, Kashif Shah |
Trans. Assoc. Comput. Linguistics | 1 |
| 2020 | Morphologically Aware Word-Level TranslationabstractWe propose a novel morphologically aware probability model for bilingual lexicon induction, which jointly models lexeme translation and inflectional morphology in a structured way.Our model exploits the basic linguistic intuition that the lexeme is the key lexical unit of meaning, while inflectional morphology provides additional syntactic information.This approach leads to substantial performance improvements-19% average improvement in accuracy across 6 language pairs over the state of the art in the supervised setting and 16% in the weakly supervised setting.As another contribution, we highlight issues associated with modern BLI that stem from ignoring inflectional morphology, and propose three suggestions for improving the task. Paula Czarnowska, Sebastian Ruder, Ryan Cotterell, Ann A. Copestake |
COLING | 1 |
| 2019 | Don't Forget the Long Tail! A Comprehensive Analysis of Morphological Generalization in Bilingual Lexicon InductionabstractPaula Czarnowska, Sebastian Ruder, Edouard Grave, Ryan Cotterell, Ann Copestake. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019. Paula Czarnowska, Sebastian Ruder, Edouard Grave, Ryan Cotterell, Ann A. Copestake |
EMNLP/IJCNLP (1) | 1 |