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2206.14729
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longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks
29 June 2022
Venelin Kovatchev
Trina Chatterjee
Venkata S Govindarajan
Jifan Chen
Eunsol Choi
Gabriella Chronis
Anubrata Das
K. Erk
Matthew Lease
Junyi Jessy Li
Yating Wu
Kyle Mahowald
AAML
ELM
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Papers citing
"longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks"
8 / 8 papers shown
Title
Think Twice: Measuring the Efficiency of Eliminating Prediction Shortcuts of Question Answering Models
Lukávs Mikula
Michal vStefánik
Marek Petrovivc
Petr Sojka
41
3
0
11 May 2023
The State of Human-centered NLP Technology for Fact-checking
Anubrata Das
Houjiang Liu
Venelin Kovatchev
Matthew Lease
HILM
24
61
0
08 Jan 2023
DALL-E 2 Fails to Reliably Capture Common Syntactic Processes
Evelina Leivada
Elliot Murphy
G. Marcus
138
37
0
23 Oct 2022
InferES : A Natural Language Inference Corpus for Spanish Featuring Negation-Based Contrastive and Adversarial Examples
Venelin Kovatchev
Mariona Taulé
33
4
0
06 Oct 2022
Frequency Effects on Syntactic Rule Learning in Transformers
Jason W. Wei
Dan Garrette
Tal Linzen
Ellie Pavlick
88
62
0
14 Sep 2021
DynaSent: A Dynamic Benchmark for Sentiment Analysis
Christopher Potts
Zhengxuan Wu
Atticus Geiger
Douwe Kiela
230
77
0
30 Dec 2020
ANLIzing the Adversarial Natural Language Inference Dataset
Adina Williams
Tristan Thrush
Douwe Kiela
AAML
174
46
0
24 Oct 2020
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
297
6,984
0
20 Apr 2018
1