Who cites «Adversarial Examples for Evaluating Reading Comprehension Systems» · page 5

579 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. ← the card

Generative Data Augmentation for Commonsense Reasoning — 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
Gradient-based Analysis of NLP Models is Manipulable — 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
Graph-Based Knowledge Integration for Question Answering over Dialogue — 2020 · Proceedings of the 28th International Conference on Computational Linguistics
How Can We Accelerate Progress Towards Human-like Linguistic Generalization? — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
How Does Context Matter? On the Robustness of Event Detection with Context-Selective Mask Generalization — 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
How State-Of-The-Art Models Can Deal With Long-Form Question Answering — 2020 · Proceedings of the 34th Pacific Asia Conference on Language, Information and Computation
Improving Grammatical Error Correction Models with Purpose-Built Adversarial Examples — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Improving QA Generalization by Concurrent Modeling of Multiple Biases — 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension — 2020 · Proceedings of the 24th Conference on Computational Natural Language Learning
Interpreting Predictions of NLP Models — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts
It's Morphin' Time! Combating Linguistic Discrimination with Inflectional Perturbations — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Iterative Feature Mining for Constraint-Based Data Collection to Increase Data Diversity and Model Robustness — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Learning Robust Models for e-Commerce Product Search — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Learning Which Features Matter: RoBERTa Acquires a Preference for Linguistic Generalizations (Eventually) — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Learning to Model and Ignore Dataset Bias with Mixed Capacity Ensembles — 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
Look at the First Sentence: Position Bias in Question Answering — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Measuring and Reducing Gendered Correlations in Pre-trained Models — 2020 · arXiv:2010.06032
Mind Your Inflections! Improving NLP for Non-Standard Englishes with Base-Inflection Encoding — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
More Bang for Your Buck: Natural Perturbation for Robust Question Answering — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Neural Natural Language Inference Models Partially Embed Theories of Lexical Entailment and Negation — our card · 2020 · Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP
Overestimation of Syntactic Representation in Neural Language Models — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Pretrained Transformers Improve Out-of-Distribution Robustness — 2020 · arXiv:2004.06100
Priorless Recurrent Networks Learn Curiously — 2020 · Proceedings of the 28th International Conference on Computational Linguistics
Research on Task Discovery for Transfer Learning in Deep Neural Networks — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
Robust Encodings: A Framework for Combating Adversarial Typos — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Robust Unsupervised Neural Machine Translation with Adversarial Denoising Training — 2020 · Proceedings of the 28th International Conference on Computational Linguistics
Robustness to Modification with Shared Words in Paraphrase Identification — 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
SQuAD2-CR: Semi-supervised Annotation for Cause and Rationales for Unanswerability in SQuAD 2.0 — 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference
Selective Question Answering under Domain Shift — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Shortcut Learning in Deep Neural Networks — 2020 · arXiv:2004.07780
Stress Test Evaluation of Transformer-based Models in Natural Language Understanding Tasks — 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference
Syntactic Data Augmentation Increases Robustness to Inference Heuristics — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP — 2020 · arXiv:2005.05909
The Curse of Performance Instability in Analysis Datasets: Consequences, Source, and Suggestions — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence — 2020 · arXiv:2002.06177
The TechQA Dataset — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Towards Debiasing NLU Models from Unknown Biases — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Towards Interpreting BERT for Reading Comprehension Based QA — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Towards Robustifying NLI Models Against Lexical Dataset Biases — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Translation Artifacts in Cross-lingual Transfer Learning — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Uncertainty Modeling for Machine Comprehension Systems using Efficient Bayesian Neural Networks — 2020 · Proceedings of the 28th International Conference on Computational Linguistics: Industry Track
Undersensitivity in Neural Reading Comprehension — 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
Utility is in the Eye of the User: A Critique of NLP Leaderboards — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
What Can We Do to Improve Peer Review in NLP? — 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
What Does My QA Model Know? Devising Controlled Probes Using Expert Knowledge — 2020 · Transactions of the Association for Computational Linguistics
What Don't RNN Language Models Learn About Filler-Gap Dependencies? — 2020 · Proceedings of the Society for Computation in Linguistics 2020
What Happens To BERT Embeddings During Fine-tuning? — 2020 · Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP
What do Models Learn from Question Answering Datasets? — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
What do we expect from Multiple-choice QA Systems? — 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
What's in a Name? Are BERT Named Entity Representations just as Good for any other Name? — 2020 · Proceedings of the 5th Workshop on Representation Learning for NLP
Word meaning in minds and machines — 2020 · arXiv:2008.01766
Word-level Textual Adversarial Attacking as Combinatorial Optimization — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
A Lightweight Recurrent Network for Sequence Modeling — 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
A Logic-Driven Framework for Consistency of Neural Models — 2019 · arXiv:1909.00126
Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation — 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering — 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Analysis Methods in Neural Language Processing: A Survey — 2019 · Transactions of the Association for Computational Linguistics
Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets — 2019 · arXiv:1911.09241
Avoiding Reasoning Shortcuts: Adversarial Evaluation, Training, and Model Development for Multi-Hop QA — 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions — 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack — 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text — 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Certified Robustness to Adversarial Word Substitutions — 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Cognitive Graph for Multi-Hop Reading Comprehension at Scale — 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Combating Adversarial Misspellings with Robust Word Recognition — 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Comprehensive Multi-Dataset Evaluation of Reading Comprehension — 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering
Deep Adversarial Learning for NLP — 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials
Detecting and Reducing Bias in a High Stakes Domain — 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Don''t Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases — 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Don''t Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference — 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Effective Adversarial Regularization for Neural Machine Translation — 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Errudite: Scalable, Reproducible, and Testable Error Analysis — 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Evaluating adversarial attacks against multiple fact verification systems — 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Evaluating and Enhancing the Robustness of Dialogue Systems: A Case Study on a Negotiation Agent — 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Explicit Utilization of General Knowledge in Machine Reading Comprehension — 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Finding Generalizable Evidence by Learning to Convince Q&A Models — 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
FreeLB: Enhanced Adversarial Training for Natural Language Understanding — 2019 · arXiv:1909.11764
HellaSwag: Can a Machine Really Finish Your Sentence? — 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Improving the Robustness of Deep Reading Comprehension Models by Leveraging Syntax Prior — 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering
Improving the Robustness of Question Answering Systems to Question Paraphrasing — 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Incorporating Priors with Feature Attribution on Text Classification — 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Inoculation by Fine-Tuning: A Method for Analyzing Challenge Datasets — 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Interactive Language Learning by Question Answering — 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Introduction to Neural Network based Approaches for Question Answering over Knowledge Graphs — 2019 · arXiv:1907.09361
JEC-QA: A Legal-Domain Question Answering Dataset — 2019 · arXiv:1911.12011
Learning and Evaluating General Linguistic Intelligence — 2019 · arXiv:1901.11373
Learning the Difference that Makes a Difference with Counterfactually-Augmented Data — 2019 · arXiv:1909.12434
Learning to Ask Unanswerable Questions for Machine Reading Comprehension — 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
LexicalAT: Lexical-Based Adversarial Reinforcement Training for Robust Sentiment Classification — 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
MLQA: Evaluating Cross-lingual Extractive Question Answering — 2019 · arXiv:1910.07475
Measuring text readability with machine comprehension: a pilot study — 2019 · Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
Misleading Failures of Partial-input Baselines — 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Multi-hop Selector Network for Multi-turn Response Selection in Retrieval-based Chatbots — 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension — 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Natural Questions: A Benchmark for Question Answering Research — 2019
On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models — 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)