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Jindřich Libovický

Charles University

26 papers hereh-index 181.5k citations55 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author2
  • first author10
  • middle author11
  • last author3

Across the 26 of 26 papers where every author was matched, so the position is known.

fields
  • cs.CL26
affiliations
  • Charles University
HomepageORCID 0000-0001-7717-4090
same name
  • Jindřich Libovický — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20162024
most citedHow Language-Neutral is Multilingual BERT?

76 citations · 106 across the 17 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

cs.CL2022★ 4 cited

CUNI Systems for the WMT22 Czech-Ukrainian Translation Task

Martin Popel, Jindřich Libovický, Jindřich Helcl

We present Charles University submissions to the WMT22 General Translation Shared Task on Czech-Ukrainian and Ukrainian-Czech machine translation. We present two constrained submis…

cs.CL2022

Speaking Multiple Languages Affects the Moral Bias of Language Models

Katharina Hämmerl, Björn Deiseroth, Patrick Schramowski +4

Pre-trained multilingual language models (PMLMs) are commonly used when dealing with data from multiple languages and cross-lingual transfer. However, PMLMs are trained on varying…

cs.CL2022★ 6 cited

Do Multilingual Language Models Capture Differing Moral Norms?

Katharina Hämmerl, Björn Deiseroth, Patrick Schramowski +3

Massively multilingual sentence representations are trained on large corpora of uncurated data, with a very imbalanced proportion of languages included in the training. This may ca…

cs.CL2022

Combining Static and Contextualised Multilingual Embeddings

Katharina Hämmerl, Jindřich Libovický, Alexander Fraser

Static and contextual multilingual embeddings have complementary strengths. Static embeddings, while less expressive than contextual language models, can be more straightforwardly…

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