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Tomasz Limisiewicz

Charles University in Prague

16 papers hereh-index 113.3k citations22 works total

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

author position
  • first author8
  • middle author7

Across the 15 of 16 papers where every author was matched, so the position is known.

fields
  • cs.CL16
affiliations
  • Charles University in Prague
ORCID 0000-0003-3809-2580
same name
  • Tomasz Limisiewicz — 8 papers, h 2
  • Tomasz Limisiewicz — 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
20202026
most citedUniversal Dependencies according to BERT: both more specific and more general

5 citations · 9 across the 13 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

cs.CL2020

Introducing Orthogonal Constraint in Structural Probes

Tomasz Limisiewicz, David Mareček

With the recent success of pre-trained models in NLP, a significant focus was put on interpreting their representations. One of the most prominent approaches is structural probing…

cs.CL2020

Gender Coreference and Bias Evaluation at WMT 2020

Tom Kocmi, Tomasz Limisiewicz, Gabriel Stanovsky

Gender bias in machine translation can manifest when choosing gender inflections based on spurious gender correlations. For example, always translating doctors as men and nurses as…

cs.CL2020★ 1 cited

Syntax Representation in Word Embeddings and Neural Networks -- A Survey

Tomasz Limisiewicz, David Mareček

Neural networks trained on natural language processing tasks capture syntax even though it is not provided as a supervision signal. This indicates that syntactic analysis is essent…

cs.CL2020★ 5 cited

Universal Dependencies according to BERT: both more specific and more general

Tomasz Limisiewicz, Rudolf Rosa, David Mareček

This work focuses on analyzing the form and extent of syntactic abstraction captured by BERT by extracting labeled dependency trees from self-attentions. Previous work showed that…

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