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SyGNS: A Systematic Generalization Testbed Based on Natural Language
  Semantics

SyGNS: A Systematic Generalization Testbed Based on Natural Language Semantics

2 June 2021
Hitomi Yanaka
K. Mineshima
Kentaro Inui
    NAI
    AI4CE
ArXivPDFHTML

Papers citing "SyGNS: A Systematic Generalization Testbed Based on Natural Language Semantics"

5 / 5 papers shown
Title
Dyna-bAbI: unlocking bAbI's potential with dynamic synthetic
  benchmarking
Dyna-bAbI: unlocking bAbI's potential with dynamic synthetic benchmarking
Ronen Tamari
Kyle Richardson
Aviad Sar-Shalom
Noam Kahlon
Nelson F. Liu
Reut Tsarfaty
Dafna Shahaf
30
5
0
30 Nov 2021
Probing Linguistic Systematicity
Probing Linguistic Systematicity
Emily Goodwin
Koustuv Sinha
Timothy J. O'Donnell
91
58
0
08 May 2020
Are We Modeling the Task or the Annotator? An Investigation of Annotator
  Bias in Natural Language Understanding Datasets
Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets
Mor Geva
Yoav Goldberg
Jonathan Berant
237
319
0
21 Aug 2019
Hypothesis Only Baselines in Natural Language Inference
Hypothesis Only Baselines in Natural Language Inference
Adam Poliak
Jason Naradowsky
Aparajita Haldar
Rachel Rudinger
Benjamin Van Durme
190
576
0
02 May 2018
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language
  Understanding
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
Alex Jinpeng Wang
Amanpreet Singh
Julian Michael
Felix Hill
Omer Levy
Samuel R. Bowman
ELM
294
6,943
0
20 Apr 2018
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