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

7 September 2018

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

8 / 8 papers shown

Title
TruthfulQA: Measuring How Models Mimic Human Falsehoods Stephanie C. Lin Jacob Hilton Owain Evans HILM 57 1,750 0 08 Sep 2021
Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision Damien Teney Ehsan Abbasnejad Anton Van Den Hengel OOD SSL CML 37 118 0 20 Apr 2020
Adversarial NLI: A New Benchmark for Natural Language Understanding Yixin Nie Adina Williams Emily Dinan Joey Tianyi Zhou Jason Weston Douwe Kiela 51 980 0 31 Oct 2019
Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey W. Zhang Quan Z. Sheng A. Alhazmi Chenliang Li AAML 24 57 0 21 Jan 2019
Interpreting Neural Networks With Nearest Neighbors Eric Wallace Shi Feng Jordan L. Boyd-Graber AAML FAtt MILM 23 53 0 08 Sep 2018
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 304 7,005 0 20 Apr 2018
Adversarial Example Generation with Syntactically Controlled Paraphrase Networks Mohit Iyyer John Wieting Kevin Gimpel Luke Zettlemoyer AAML GAN 205 713 0 17 Apr 2018
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017