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Adversarial Examples for Evaluating Reading Comprehension Systems
Robin Jia
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Percy Liang
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Paper Details:
Month: September
Year: 2017
Location: Copenhagen, Denmark
Venue:
EMNLP |
Citations
URL
Answerable or Not: Devising a Dataset for Extending Machine Reading Comprehension
Mao Nakanishi
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Tetsunori Kobayashi
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Yoshihiko Hayashi
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Learning Visually-Grounded Semantics from Contrastive Adversarial Samples
Haoyue Shi
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Jiayuan Mao
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Tete Xiao
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Yuning Jiang
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Jian Sun
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Adversarial Evaluation of Multimodal Machine Translation
Desmond Elliott
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Pathologies of Neural Models Make Interpretations Difficult
Shi Feng
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Eric Wallace
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Alvin Grissom II
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Mohit Iyyer
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Pedro Rodriguez
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Jordan Boyd-Graber
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What Makes Reading Comprehension Questions Easier?
Saku Sugawara
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Kentaro Inui
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Satoshi Sekine
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Akiko Aizawa
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The Importance of Being Recurrent for Modeling Hierarchical Structure
Ke Tran
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Arianna Bisazza
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Christof Monz
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An Interactive Web-Interface for Visualizing the Inner Workings of the Question Answering LSTM
Ekaterina Loginova
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Günter Neumann
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Adversarially Regularising Neural NLI Models to Integrate Logical Background Knowledge
Pasquale Minervini
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Sebastian Riedel
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Comparing Attention-Based Convolutional and Recurrent Neural Networks: Success and Limitations in Machine Reading Comprehension
Matthias Blohm
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Glorianna Jagfeld
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Ekta Sood
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Xiang Yu
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Ngoc Thang Vu
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Adversarial Over-Sensitivity and Over-Stability Strategies for Dialogue Models
Tong Niu
|
Mohit Bansal
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Using Adversarial Examples in Natural Language Processing
Petr Bělohlávek
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Ondřej Plátek
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Zdeněk Žabokrtský
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Milan Straka
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Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences
Daniel Khashabi
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Snigdha Chaturvedi
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Michael Roth
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Shyam Upadhyay
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Dan Roth
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The Web as a Knowledge-Base for Answering Complex Questions
Alon Talmor
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Jonathan Berant
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Tracking State Changes in Procedural Text: a Challenge Dataset and Models for Process Paragraph Comprehension
Bhavana Dalvi
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Lifu Huang
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Niket Tandon
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Wen-tau Yih
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Peter Clark
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Adversarial Example Generation with Syntactically Controlled Paraphrase Networks
Mohit Iyyer
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John Wieting
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Kevin Gimpel
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Luke Zettlemoyer
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Contextualized Word Representations for Reading Comprehension
Shimi Salant
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Jonathan Berant
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Robust Machine Comprehension Models via Adversarial Training
Yicheng Wang
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Mohit Bansal
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Semantically Equivalent Adversarial Rules for Debugging NLP models
Marco Tulio Ribeiro
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Sameer Singh
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Carlos Guestrin
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LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better
Adhiguna Kuncoro
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Chris Dyer
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John Hale
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Dani Yogatama
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Stephen Clark
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Phil Blunsom
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Stochastic Answer Networks for Machine Reading Comprehension
Xiaodong Liu
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Yelong Shen
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Kevin Duh
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Jianfeng Gao
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Multi-Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question Answering
Wei Wang
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Ming Yan
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Chen Wu
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Efficient and Robust Question Answering from Minimal Context over Documents
Sewon Min
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Victor Zhong
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Richard Socher
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Caiming Xiong
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Did the Model Understand the Question?
Pramod Kaushik Mudrakarta
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Ankur Taly
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Mukund Sundararajan
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Kedar Dhamdhere
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Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification
Yizhong Wang
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Kai Liu
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Jing Liu
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Wei He
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Yajuan Lyu
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Hua Wu
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Sujian Li
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Haifeng Wang
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AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples
Dongyeop Kang
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Tushar Khot
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Ashish Sabharwal
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Eduard Hovy
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HotFlip: White-Box Adversarial Examples for Text Classification
Javid Ebrahimi
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Anyi Rao
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Daniel Lowd
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Dejing Dou
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Know What You Don’t Know: Unanswerable Questions for SQuAD
Pranav Rajpurkar
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Robin Jia
|
Percy Liang
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Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions
Eric Wallace
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Jordan Boyd-Graber
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Mapping to Declarative Knowledge for Word Problem Solving
Subhro Roy
|
Dan Roth
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Constructing Datasets for Multi-hop Reading Comprehension Across Documents
Johannes Welbl
|
Pontus Stenetorp
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Sebastian Riedel
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Analysis Methods in Neural Language Processing: A Survey
Yonatan Belinkov
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James Glass
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Systematic Error Analysis of the Stanford Question Answering Dataset
Marc-Antoine Rondeau
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T. J. Hazen
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Tackling Adversarial Examples in QA via Answer Sentence Selection
Yuanhang Ren
|
Ye Du
|
Di Wang
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Nightmare at test time: How punctuation prevents parsers from generalizing
Anders Søgaard
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Miryam de Lhoneux
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Isabelle Augenstein
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How much should you ask? On the question structure in QA systems.
Barbara Rychalska
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Dominika Basaj
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Anna Wróblewska
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Przemyslaw Biecek
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Does it care what you asked? Understanding Importance of Verbs in Deep Learning QA System
Barbara Rychalska
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Dominika Basaj
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Anna Wróblewska
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Przemyslaw Biecek
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Teaching Syntax by Adversarial Distraction
Juho Kim
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Christopher Malon
|
Asim Kadav
|
https://rajpurkar.github.io/
Field Of Study
Task
Language Understanding
Approach
Deep Learning
Language
English
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