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Roy Schwartz

32 papers hereh-index 327.7k citations41 works total

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

author position
  • first author4
  • middle author17
  • last author9

Across the 30 of 32 papers where every author was matched, so the position is known.

fields
  • cs.CL28
  • cs.LG2
  • cs.CV1
  • cs.SE1
same name
  • Roy Schwartz — 18 papers, h 12
  • Roy Schwartz — 11 papers, h 18
  • Roy Schwartz — 7 papers, h 2
  • Roy Schwartz — 6 papers, h 4
  • Roy Schwartz — 4 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
20172025
most citedFine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

215 citations · 411 across the 19 of their papers we have counts for

collaborators
Showing 2018Show all

4 papers · 1 filter

cs.CL2018

SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference

Rowan Zellers, Yonatan Bisk, Roy Schwartz +1

Given a partial description like "she opened the hood of the car," humans can reason about the situation and anticipate what might come next ("then, she examined the engine"). In t…

cs.CL2018

SoPa: Bridging CNNs, RNNs, and Weighted Finite-State Machines

Roy Schwartz, Sam Thomson, Noah A. Smith

Recurrent and convolutional neural networks comprise two distinct families of models that have proven to be useful for encoding natural language utterances. In this paper we presen…

cs.CL2018

A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications

Dongyeop Kang, Waleed Ammar, Bhavana Dalvi +4

Peer reviewing is a central component in the scientific publishing process. We present the first public dataset of scientific peer reviews available for research purposes (PeerRead…

cs.CL2018

Annotation Artifacts in Natural Language Inference Data

Suchin Gururangan, Swabha Swayamdipta, Omer Levy +3

Large-scale datasets for natural language inference are created by presenting crowd workers with a sentence (premise), and asking them to generate three new sentences (hypotheses)…

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