Who cites «Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference» · page 7

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

Adversarial Filters of Dataset Biases — 2020 · arXiv:2002.04108
Adversarial NLI: A New Benchmark for Natural Language Understanding — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
An Empirical Study on Model-agnostic Debiasing Strategies for Robust Natural Language Inference — 2020 · Proceedings of the 24th Conference on Computational Natural Language Learning
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models — 2020
An empirical analysis of existing systems and datasets toward general simple question answering — 2020 · Proceedings of the 28th International Conference on Computational Linguistics
Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition — our card · 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Are Pretrained Language Models Symbolic Reasoners over Knowledge? — 2020 · Proceedings of the 24th Conference on Computational Natural Language Learning
Asking Crowdworkers to Write Entailment Examples: The Best of Bad Options — 2020 · Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing
BERTnesia: Investigating the capture and forgetting of knowledge in BERT — 2020 · Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP
BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance — 2020 · Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP
Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models — 2020 · arXiv:2005.14709
CLUE: A Chinese Language Understanding Evaluation Benchmark — 2020 · arXiv:2004.05986
COGS: A Compositional Generalization Challenge Based on Semantic Interpretation — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Can Pre-training help VQA with Lexical Variations? — 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERT — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Combining Event Semantics and Degree Semantics for Natural Language Inference — our card · 2020 · Proceedings of the 28th International Conference on Computational Linguistics
ConjNLI: Natural Language Inference Over Conjunctive Sentences — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Constrained Fact Verification for FEVER — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
ContraCAT: Contrastive Coreference Analytical Templates for Machine Translation — 2020 · Proceedings of the 28th International Conference on Computational Linguistics
Detecting Entailment in Code-Mixed Hindi-English Conversations — 2020 · Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
Detecting Word Sense Disambiguation Biases in Machine Translation for Model-Agnostic Adversarial Attacks — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Discovering the Compositional Structure of Vector Representations with Role Learning Networks — 2020 · Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP
Discriminatively-Tuned Generative Classifiers for Robust Natural Language Inference — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Do Neural Models Learn Systematicity of Monotonicity Inference in Natural Language? — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Emergence of Syntax Needs Minimal Supervision — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Encodings of Source Syntax: Similarities in NMT Representations Across Target Languages — 2020 · Proceedings of the 5th Workshop on Representation Learning for NLP
End-to-End Bias Mitigation by Modelling Biases in Corpora — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Enhancing Generalization in Natural Language Inference by Syntax — 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
Evaluating German Transformer Language Models with Syntactic Agreement Tests — 2020 · arXiv:2007.03765 · Proceedings of the 5th Swiss Text Analytics Conference and the 16th Conference on Natural Language Processing, SwissText/KONVENS 2020, Zurich, Switzerland, June 23-25, 2020 [online only]. CEUR Workshop Proceedings 2624
Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Exposing Shallow Heuristics of Relation Extraction Models with Challenge Data — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Frequency, acceptability, and selection: A case study of clause-embedding — 2020 · Glossa: a journal of general 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 Relevant Are Selectional Preferences for Transformer-based Language Models? — 2020 · Proceedings of the 28th International Conference on Computational Linguistics
Hy-NLI: a Hybrid system for Natural Language Inference — 2020 · Proceedings of the 28th International Conference on Computational Linguistics
HypoNLI: Exploring the Artificial Patterns of Hypothesis-only Bias in Natural Language Inference — 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference
Improving QA Generalization by Concurrent Modeling of Multiple Biases — 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
Inducing Grammar from Long Short-Term Memory Networks by Shapley Decomposition — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
Interpreting Predictions of NLP Models — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts
Intrinsic Evaluation of Summarization Datasets — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Joint Training with Semantic Role Labeling for Better Generalization in Natural Language Inference — 2020 · Proceedings of the 5th Workshop on Representation Learning for NLP
Language (Re)modelling: Towards Embodied Language Understanding — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Language Models are Few-Shot Learners — 2020 · arXiv:2005.14165
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
Linguistically-Informed Transformations (LIT): A Method for Automatically Generating Contrast Sets — 2020 · Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP
Logical Inferences with Comparatives and Generalized Quantifiers — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
Look at the First Sentence: Position Bias in Question Answering — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question Answering — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Masking as an Efficient Alternative to Finetuning for Pretrained Language Models — 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 the Trade-off: Debiasing NLU Models without Degrading the In-distribution Performance — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
MonaLog: a Lightweight System for Natural Language Inference Based on Monotonicity — 2020 · Proceedings of the Society for Computation in Linguistics 2020
NEU at WNUT-2020 Task 2: Data Augmentation To Tell BERT That Death Is Not Necessarily Informative — 2020 · Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
OCNLI: Original Chinese Natural Language Inference — 2020 · arXiv:2010.05444
On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks — 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines — 2020 · arXiv:2006.04884
On the Systematicity of Probing Contextualized Word Representations: The Case of Hypernymy in BERT — 2020 · Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics
Picking BERT''s Brain: Probing for Linguistic Dependencies in Contextualized Embeddings Using Representational Similarity Analysis — 2020 · Proceedings of the 28th International Conference on Computational Linguistics
Probing Linguistic Systematicity — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Shortcut Learning in Deep Neural Networks — 2020 · arXiv:2004.07780
Syntactic Data Augmentation Increases Robustness to Inference Heuristics — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
TaxiNLI: Taking a Ride up the NLU Hill — 2020 · Proceedings of the 24th Conference on Computational Natural Language Learning
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 Sensitivity of Language Models and Humans to Winograd Schema Perturbations — 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 Non-task-specific Distillation of BERT via Sentence Representation Approximation — 2020 · Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing
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)
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 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 is More Likely to Happen Next? Video-and-Language Future Event Prediction — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
When BERT Plays the Lottery, All Tickets Are Winning — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
XplaiNLI: Explainable Natural Language Inference through Visual Analytics — 2020 · Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations
Adversarial Analysis of Natural Language Inference Systems — 2019 · arXiv:1912.03441
An Annotated Corpus of Reference Resolution for Interpreting Common Grounding — 2019 · arXiv:1911.07588
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)
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)
Evaluating BERT for natural language inference: A case study on the CommitmentBank — 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)
How well do NLI models capture verb veridicality? — our card · 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)
Inherent Disagreements in Human Textual Inferences — our card · 2019
Language Models as Knowledge Bases? — 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)
Probing Natural Language Inference Models through Semantic Fragments — 2019 · arXiv:1909.07521
Probing Neural Network Comprehension of Natural Language Arguments — 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Ranking Generated Summaries by Correctness: An Interesting but Challenging Application for Natural Language Inference — 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems — 2019 · arXiv:1905.00537
Towards Debiasing Fact Verification 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)
Universal Adversarial Triggers for Attacking and Analyzing NLP — 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)
Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual — 2019 · Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)
Visually grounded generation of entailments from premises — 2019 · Proceedings of the 12th International Conference on Natural Language Generation
When Choosing Plausible Alternatives, Clever Hans can be Clever — 2019 · Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing