🇨🇿 🇪🇺 Researcher at @ufal_cuni. Working on multilingual NLP and neural machine translation. Views my own. He/him

Joined July 2011
38 Photos and videos
Join Mu-SHROOM 🍄, a SemEval 2025 shared task on detecting hallucination spans in multilingual LLM outputs! 🌍 Includes Czech with regional Czech questions 🇨🇿. Do you think you can spot when something isn’t true? 🤔 Try it out! 👉 helsinki-nlp.github.io/shroo… #SemEval2025 #NLProc
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Happy holidays! 🎄🎅🤩🎁
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This is going to be fun! 🤓 We have three years to spend 6.5M CZK on improving multilingual tokenization. The goal is to make subwords more alignable across languages and help languages that suffer from over-segmentation with current models.
Good news! 🥳 GAČR will fund two of our projects: 👉 @jlibovicky proposes to better tokenization for #LLMs and machine translation 👉 Veronika Kolářová will study syntactic features of Czech non-verbal predicates ➕ Dominik Macháček receives Postdoc Individual Fellowship! 💪
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Find me on 🦋 (and the rest of #NLProc folks too).
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There's no clear winner this year's MRL shared task, but we ended up in the cluseer of top-3 teams. I'm so proud of you, folks ☺️
Replying to @ufal_cuni
Finally, @kat_haem and Gianluca Vico presented one of the three price-winning 🏆🤑 submissons for the shared task on multilingual named entity recognition and question answering! w/ @AndreiM85400815, @jindra_helcl and @jlibovicky. Congrats! aclanthology.org/2024.mrl-1.…
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Thanks to everyone who stopped by the poster ☺️
#EMNLP2024 starts today and @ufal_cuni is here! We start with @jlibovicky presenting work with @jindra_helcl: Lexically Grounded Subword Segmentation aclanthology.org/2024.emnlp-…
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This week I am at #EMNLP2024 in Miami 🌴🇺🇸. Find me 🕵️ or message 💌 me if you want to chat about multilinguality or tokenization and stop by our poster on Tuesday at 2 p.m., I'll present our paper on lexically Grounded Subword Segmentation aclanthology.org/2024.emnlp-…
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Jindřich Libovický retweeted
... starring @jlibovicky and me as young and perspective scientists with their impeccable movie editing skills
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In a week, @jindra_helcl and I will present our paper Lexically Grounded Subword Segmentation at #EMNLP2024 in Miami 🌴🇺🇸. You can already watch our video 🎥 youtube.com/watch?v=NjUmNg6U… or stop by our poster 👋 next Tuesday at 2 p.m...
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If you liked the video, read our paper arxiv.org/abs/2406.13560 or check our code github.com/ufal/legros-paper x.com/jlibovicky/status/1842…

In our #EMNLP2024 paper with @jindra_helcl, we present a new subword tokenization method that is more morphologically plausible but maintains the nice properties of existing tokenizers. Pre-print: arxiv.org/pdf/2406.13560 Code: github.com/ufal/legros 👇🧵1/4
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In our #EMNLP2024 paper with @jindra_helcl, we present a new subword tokenization method that is more morphologically plausible but maintains the nice properties of existing tokenizers. Pre-print: arxiv.org/pdf/2406.13560 Code: github.com/ufal/legros 👇🧵1/4
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Then, we find segmentations with subwords with the closest embedding closest to the word embedding. We collect bigram stats from those and use them in a bigram-LM-based segmenter (a generalization of SentencePiece). And we also do some experiments... 🧵3/4
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👍 It works great for preserving morpheme boundaries. 👍 Does a good job in POS tagging. 👎 No improvement in machine translation. And bad news, @zouharvi, our downstream performance does not correlate with Rényi efficiency. 🤷‍♂️ 🧵4/4
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📣 We have a dataset! ❓Have you also noticed that language-vision encoders like CLIP do not pay attention to details? ❓ Do you think your model is doing better? 👉 InpaintCOCO dataset huggingface.co/datasets/phiy… is here for you. Work of @phiyodr, folks from @unibw_m, and myself.
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It consists of minimum pairs of images and captions derived from the MS COCO test set. Annotators used object detection and Stable Diffusion Inpanting 👨‍🎨👩‍🎨 to get images with either different objects or objects of different colors and sizes. Everything's 100% human-supervised. 💪
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In the paper introducing the dataset aclanthology.org/2024.alvr-1…, we also present a method based on hard-negative sampling on the text side of the model that significantly improves the model's ability to distinguish details.
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