Papers by Jindřich Helcl
Lexically Grounded Subword Segmentation (2024.emnlp-main)
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| Challenge: | Statistical word segmentation algorithms have remained a thorn in the side of many researchers. |
| Approach: | They propose to use unsupervised morphological analysis with Morfessor as pre-tokenization and an algebraic method for obtaining subword embeddings grounded in a word embeddable space. |
| Outcome: | The proposed methods improve morphological plausibility and Rényi efficiency on part-of-speech tagging and machine translation tasks. |
Different Time, Different Language: Revisiting the Bias Against Non-Native Speakers in GPT Detectors (2026.eacl-srw)
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| Challenge: | Prior work suggests that automated methods falsely flag essays from non-native speakers as generated due to their low perplexity extracted from an LLM, which is supposedly a key feature of the detectors. |
| Approach: | They propose to use a perplexity-based detector to detect essays from non-native speakers of Czech and a detector to examine the effects of different families. |
| Outcome: | The proposed methods are not biased against non-native speakers, but instead falsely flag essays from non-natural speakers as generated, compared to the English essays. |
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 . |
Non-Autoregressive Machine Translation: It’s Not as Fast as it Seems (2022.naacl-main)
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| Challenge: | Efficient machine translation models are commercially important as they can increase inference speeds, reduce costs and carbon emissions. |
| Approach: | They compare NAR models with autoregressive models to evaluate their performance . they point out flaws in evaluation methodology and argue for consistent evaluation . |
| Outcome: | The proposed model is faster on GPUs, but slower under more realistic usage conditions. |
End-to-End Non-Autoregressive Neural Machine Translation with Connectionist Temporal Classification (D18-1)
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| Challenge: | Autoregressive decoding is the only part of sequence-to-sequence models that prevents massive parallelization at inference time. |
| Approach: | They propose a non-autoregressive architecture based on connectionist temporal classification . they conduct experiments on the WMT English-Romanian and English-German datasets . |
| Outcome: | The proposed model achieves a significant speedup over autoregressive models . the model can be trained end-to-end and maintains translation quality comparable to other models compared to autoregression models based on connectionist temporal classification . |
Charles Translator: A Machine Translation System between Ukrainian and Czech (2024.lrec-main)
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Martin Popel, Lucie Polakova, Michal Novák, Jindřich Helcl, Jindřich Libovický, Pavel Straňák, Tomas Krabac, Jaroslava Hlavacova, Mariia Anisimova, Tereza Chlanova
| Challenge: | a system for translating between Ukrainian and Czech was developed in the spring of 2022 . the system was not available at the time in the required quality . |
| Approach: | They propose a machine translation system between Ukrainian and Czech to reduce the impact of the Russian-Ukrainian war on individuals and society. |
| Outcome: | The proposed system translates directly between Ukrainian and Czech, compared to other systems that use English as a pivot. |
Thesis Proposal: Targeted and Unified Cross-Lingual Unlearning from Multilingual Language Models (2026.acl-srw)
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| Challenge: | Large language models trained on corpora scraped from the web can reproduce sensitive and copyright-protected data. |
| Approach: | They propose to extend existing benchmarks to multilingual data by compiling parallel translations of question-answer pairs consisting of real-world facts and synthetic personally identifiable information. |
| Outcome: | The proposed dataset will include translations of question-answer pairs consisting of real-world facts and synthetic personally identifiable information. |