Papers by Dušan Variš
Improving a Neural-based Tagger for Multiword Expressions Identification (L18-1)
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| Challenge: | MUMULS tagger for automatic detection of verbal multiword expressions is based on neural networks . character-level embeddings can improve the performance, reducing out-of-vocabulary rate . multiword Expressions are viewed by computational linguists as a "pain in the neck of NLP" |
| Approach: | They propose to improve MUMULS, a tagger for automatic detection of verbal multiword expressions. |
| Outcome: | The proposed tagger performed better on Czech language than the previous taggers. |
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 . |
TokCollate: A Comprehensive Tool for Tokenizer Evaluation and Visualization across Languages (2026.acl-demo)
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| Challenge: | Tokenization quality varies significantly across languages, leading to disparities in LLM performance and cost for speakers of less-resourced languages. |
| Approach: | They propose a Python-based evaluation framework and a JavaScript visualization interface that evaluates tokenizers in a variety of languages. |
| Outcome: | TokCollate is an evaluation framework for tokenizers with a JavaScript visualization interface. |
Unsupervised Pretraining for Neural Machine Translation Using Elastic Weight Consolidation (P19-2)
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| Challenge: | Neural machine translation (NMT) uses sequence to sequence architectures, but requires a huge amount of parallel data. |
| Approach: | They use Elastic Weight Consolidation to regularize weights of two language models . they then fine-tune the model on parallel data to avoid forgetting the original task . |
| Outcome: | The proposed method achieves BLEU scores similar to the previous work, but is slower and requires less training data. |