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Trick Me If You Can: Human-in-the-loop Generation of Adversarial
  Examples for Question Answering
v1v2v3v4 (latest)

Trick Me If You Can: Human-in-the-loop Generation of Adversarial Examples for Question Answering

7 September 2018
Eric Wallace
Pedro Rodriguez
Shi Feng
Ikuya Yamada
Jordan L. Boyd-Graber
    AAML
ArXiv (abs)PDFHTML

Papers citing "Trick Me If You Can: Human-in-the-loop Generation of Adversarial Examples for Question Answering"

11 / 11 papers shown
Title
TruthfulQA: Measuring How Models Mimic Human Falsehoods
TruthfulQA: Measuring How Models Mimic Human Falsehoods
Stephanie C. Lin
Jacob Hilton
Owain Evans
HILM
151
1,951
0
08 Sep 2021
QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering
  and Reading Comprehension
QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension
Anna Rogers
Matt Gardner
Isabelle Augenstein
135
168
0
27 Jul 2021
Learning What Makes a Difference from Counterfactual Examples and
  Gradient Supervision
Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision
Damien Teney
Ehsan Abbasnejad
Anton Van Den Hengel
OODSSLCML
93
119
0
20 Apr 2020
Adversarial NLI: A New Benchmark for Natural Language Understanding
Adversarial NLI: A New Benchmark for Natural Language Understanding
Yixin Nie
Adina Williams
Emily Dinan
Joey Tianyi Zhou
Jason Weston
Douwe Kiela
189
1,013
0
31 Oct 2019
A Visual Analytics Framework for Adversarial Text Generation
A Visual Analytics Framework for Adversarial Text Generation
Brandon Laughlin
C. Collins
K. Sankaranarayanan
K. El-Khatib
AAML
37
10
0
24 Sep 2019
Detecting and Reducing Bias in a High Stakes Domain
Detecting and Reducing Bias in a High Stakes Domain
Ruiqi Zhong
Yanda Chen
D. Patton
C. Selous
Kathy McKeown
34
6
0
29 Aug 2019
Quizbowl: The Case for Incremental Question Answering
Quizbowl: The Case for Incremental Question Answering
Pedro Rodriguez
Shi Feng
Mohit Iyyer
He He
Jordan L. Boyd-Graber
67
50
0
09 Apr 2019
White-to-Black: Efficient Distillation of Black-Box Adversarial Attacks
White-to-Black: Efficient Distillation of Black-Box Adversarial Attacks
Yotam Gil
Yoav Chai
O. Gorodissky
Jonathan Berant
MLAUAAML
50
46
0
04 Apr 2019
Adversarial Attacks on Deep Learning Models in Natural Language
  Processing: A Survey
Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey
W. Zhang
Quan Z. Sheng
A. Alhazmi
Chenliang Li
AAML
114
57
0
21 Jan 2019
What can AI do for me: Evaluating Machine Learning Interpretations in
  Cooperative Play
What can AI do for me: Evaluating Machine Learning Interpretations in Cooperative Play
Shi Feng
Jordan L. Boyd-Graber
HAI
82
130
0
23 Oct 2018
Interpreting Neural Networks With Nearest Neighbors
Interpreting Neural Networks With Nearest Neighbors
Eric Wallace
Shi Feng
Jordan L. Boyd-Graber
AAMLFAttMILM
140
54
0
08 Sep 2018
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