Papers by Mariia Fedorova
Enriching Word Usage Graphs with Cluster Definitions (2024.lrec-main)
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| Challenge: | Existing word usage graphs lack human interpretability of senses. |
| Approach: | They propose to enrich existing word usage graphs with cluster labels functioning as sense definitions. |
| Outcome: | The proposed dataset matches the definitions chosen from WordNet by two baseline systems. |
Explaining novel senses using definition generation with open language models (2025.findings-emnlp)
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| Challenge: | We apply definition generators based on open-weights large language models to create explanations of novel senses. |
| Approach: | They apply open-weights large language models to create explanations of novel senses using target word usages as input. |
| Outcome: | The proposed definition generators perform on par with decoder-only models. |
An Expanded Massive Multilingual Dataset for High-Performance Language Technologies (HPLT) (2025.acl-long)
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Laurie Burchell, Ona De Gibert Bonet, Nikolay Arefyev, Mikko Aulamo, Marta Bañón, Pinzhen Chen, Mariia Fedorova, Liane Guillou, Barry Haddow, Jan Hajič, Jindřich Helcl, Erik Henriksson, Mateusz Klimaszewski, Ville Komulainen, Andrey Kutuzov, Joona Kytöniemi, Veronika Laippala, Petter Mæhlum, Bhavitvya Malik, Farrokh Mehryary, Vladislav Mikhailov, Nikita Moghe, Amanda Myntti, Dayyán O’Brien, Stephan Oepen, Proyag Pal, Jousia Piha, Sampo Pyysalo, Gema Ramírez-Sánchez, David Samuel, Pavel Stepachev, Jörg Tiedemann, Dušan Variš, Tereza Vojtěchová, Jaume Zaragoza-Bernabeu
| Challenge: | a large number of textual data is needed to train state-of-the-art large language models. |
| Approach: | They propose a collection of monolingual and parallel corpora from the Internet Archive . they document the entire data pipeline and release the code to reproduce it . |
| Outcome: | The proposed collection of monolingual and parallel corpora is based on the HPLT v2 dataset . it includes 8T tokens covering 193 languages and 380M sentence pairs covering 51 languages . |
Definition generation for lexical semantic change detection (2024.findings-acl)
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| Challenge: | a number of studies have attempted to bridge the gap between lexical semantic change detection and sense-based LSCD methods. |
| Approach: | They propose a sense distribution based LSCD method which uses contextualized word definitions as 'senses' they argue that the method preserves interpretability and allows to inspect the reasons behind a specific shift in terms of discrete definitions-as-sense. |
| Outcome: | The proposed method outperforms previous sense-based methods on five datasets and three languages and preserves interpretability and allows to inspect the reasons behind a specific shift in terms of discrete definitions-as-senses. |