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Roy Schwartz

The Hebrew University of Jerusalem

18 papers hereh-index 122k citations24 works total

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

author position
  • first author1
  • middle author11
  • last author5

Across the 17 of 18 papers where every author was matched, so the position is known.

fields
  • cs.CL13
  • cs.CV2
  • cs.LG2
  • cs.CY1
affiliations
  • The Hebrew University of Jerusalem
Homepage
same name
  • Roy Schwartz — 32 papers, h 32
  • Roy Schwartz — 15 papers, h 18
  • Roy Schwartz — 9 papers, h 2
  • Roy Schwartz — 6 papers, h 4
  • Roy Schwartz — 3 papers
  • Roy Schwartz — 3 papers, h 1

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
20162026
most citedGreen AI

78 citations · 102 across the 15 of their papers we have counts for

collaborators
Showing 2023 · cs.CLShow all

4 papers · 2 filters

cs.CL2023★ 11 cited

Morphosyntactic probing of multilingual BERT models

Judit Acs, Endre Hamerlik, Roy Schwartz +2

We introduce an extensive dataset for multilingual probing of morphological information in language models (247 tasks across 42 languages from 10 families), each consisting of a se…

cs.CL2023

Finding the SWEET Spot: Analysis and Improvement of Adaptive Inference in Low Resource Settings

Daniel Rotem, Michael Hassid, Jonathan Mamou +1

Adaptive inference is a simple method for reducing inference costs. The method works by maintaining multiple classifiers of different capacities, and allocating resources to each t…

cs.CL2023★ 1 cited

Surveying (Dis)Parities and Concerns of Compute Hungry NLP Research

Ji-Ung Lee, Haritz Puerto, Betty van Aken +8

Many recent improvements in NLP stem from the development and use of large pre-trained language models (PLMs) with billions of parameters. Large model sizes makes computational cos…

cs.CL2023

Fighting Bias with Bias: Promoting Model Robustness by Amplifying Dataset Biases

Yuval Reif, Roy Schwartz

NLP models often rely on superficial cues known as dataset biases to achieve impressive performance, and can fail on examples where these biases do not hold. Recent work sought to…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.