Who cites «Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference» · page 5

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

InferES : A Natural Language Inference Corpus for Spanish Featuring Negation-Based Contrastive and Adversarial Examples — 2022 · Proceedings of the 29th International Conference on Computational Linguistics
Informativeness and Invariance: Two Perspectives on Spurious Correlations in Natural Language — 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Instruction Induction: From Few Examples to Natural Language Task Descriptions — 2022 · arXiv:2205.10782
Interventional Training for Out-Of-Distribution Natural Language Understanding — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Investigating Ensemble Methods for Model Robustness Improvement of Text Classifiers — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
Investigating Reasons for Disagreement in Natural Language Inference — 2022 · Transactions of the Association for Computational Linguistics
Investigating Selective Prediction Approaches Across Several Tasks in IID, OOD, and Adversarial Settings — 2022 · Findings of the Association for Computational Linguistics: ACL 2022
Investigating the Performance of Transformer-Based NLI Models on Presuppositional Inferences — 2022 · Proceedings of the 29th International Conference on Computational Linguistics
Is Character Trigram Overlapping Ratio Still the Best Similarity Measure for Aligning Sentences in a Paraphrased Corpus? — 2022 · Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)
Kernel-Whitening: Overcome Dataset Bias with Isotropic Sentence Embedding — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
KoBEST: Korean Balanced Evaluation of Significant Tasks — 2022 · Proceedings of the 29th International Conference on Computational Linguistics
Language Models Are Poor Learners of Directional Inference — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
Language Models Understand Us, Poorly — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations — 2022 · arXiv:2204.02937
Learning Argument Structures with Recurrent Neural Network Grammars — 2022 · Proceedings of the Society for Computation in Linguistics 2022
Legal Prompt Engineering for Multilingual Legal Judgement Prediction — 2022 · arXiv:2212.02199
Less Is Better: Recovering Intended-Feature Subspace to Robustify NLU Models — 2022 · Proceedings of the 29th International Conference on Computational Linguistics
Let the Model Decide its Curriculum for Multitask Learning — 2022 · Proceedings of the Third Workshop on Deep Learning for Low-Resource Natural Language Processing
Lexical Generalization Improves with Larger Models and Longer Training — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
Life after BERT: What do Other Muppets Understand about Language? — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Logical Reasoning with Span-Level Predictions for Interpretable and Robust NLI Models — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Look to the Right: Mitigating Relative Position Bias in Extractive Question Answering — 2022 · Proceedings of the Fifth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP
Looking at the Overlooked: An Analysis on the Word-Overlap Bias in Natural Language Inference — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Machine Reading, Fast and Slow: When Do Models ``Understand'''' Language? — 2022 · Proceedings of the 29th International Conference on Computational Linguistics
Measure and Improve Robustness in NLP Models: A Survey — 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Methods for Estimating and Improving Robustness of Language Models — 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop
Mitigating Dataset Artifacts in Natural Language Inference Through Automatic Contextual Data Augmentation and Learning Optimization — 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference
Mitigating Spurious Correlation in Natural Language Understanding with Counterfactual Inference — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants — 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
NaturalAdversaries: Can Naturalistic Adversaries Be as Effective as Artificial Adversaries? — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
NeuroCounterfactuals: Beyond Minimal-Edit Counterfactuals for Richer Data Augmentation — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
Not another Negation Benchmark: The NaN-NLI Test Suite for Sub-clausal Negation — 2022 · Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
On Length Divergence Bias in Textual Matching Models — 2022 · Findings of the Association for Computational Linguistics: ACL 2022
On the data requirements of probing — 2022 · Findings of the Association for Computational Linguistics: ACL 2022
One Wug, Two Wug+s Transformer Inflection Models Hallucinate Affixes — 2022 · Proceedings of the Fifth Workshop on the Use of Computational Methods in the Study of Endangered Languages
PAIGE: Personalized Adaptive Interactions Graph Encoder for Query Rewriting in Dialogue Systems — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track
Partial-input baselines show that NLI models can ignore context, but they don't. — 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Predicting Fine-Tuning Performance with Probing — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Probing for targeted syntactic knowledge through grammatical error detection — 2022 · Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL)
Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words — 2022 · Proceedings of the 29th International Conference on Computational Linguistics
Prompting GPT-3 To Be Reliable — 2022 · arXiv:2210.09150
QA Domain Adaptation using Hidden Space Augmentation and Self-Supervised Contrastive Adaptation — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Quantifying Adaptability in Pre-trained Language Models with 500 Tasks — 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
RAIL-KD: RAndom Intermediate Layer Mapping for Knowledge Distillation — 2022 · Findings of the Association for Computational Linguistics: NAACL 2022
Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text — 2022 · arXiv:2202.06935
Retrieval-guided Counterfactual Generation for QA — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Right for the Right Reason: Evidence Extraction for Trustworthy Tabular Reasoning — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Robust Task-Oriented Dialogue Generation with Contrastive Pre-training and Adversarial Filtering — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
RobustLR: A Diagnostic Benchmark for Evaluating Logical Robustness of Deductive Reasoners — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Robustifying Sentiment Classification by Maximally Exploiting Few Counterfactuals — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
SEAL: Interactive Tool for Systematic Error Analysis and Labeling — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Semantically Distributed Robust Optimization for Vision-and-Language Inference — 2022 · Findings of the Association for Computational Linguistics: ACL 2022
Shortcut Learning of Large Language Models in Natural Language Understanding — 2022 · arXiv:2208.11857
Simple but Challenging: Natural Language Inference Models Fail on Simple Sentences — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
State-of-the-art generalisation research in NLP: A taxonomy and review — 2022 · arXiv:2210.03050 · Nat Mach Intell 5, 1161-1174 (2023)
Stretching Sentence-pair NLI Models to Reason over Long Documents and Clusters — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
Structural Persistence in Language Models: Priming as a Window into Abstract Language Representations — 2022 · Transactions of the Association for Computational Linguistics
Tailor: Generating and Perturbing Text with Semantic Controls — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Task-guided Disentangled Tuning for Pretrained Language Models — 2022 · Findings of the Association for Computational Linguistics: ACL 2022
Testing Large Language Models on Compositionality and Inference with Phrase-Level Adjective-Noun Entailment — 2022 · Proceedings of the 29th International Conference on Computational Linguistics
Testing Pre-trained Language Models' Understanding of Distributivity via Causal Mediation Analysis — 2022 · Proceedings of the Fifth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP
Text Characterization Toolkit (TCT) — 2022 · Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: System Demonstrations
Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning — 2022 · Findings of the Association for Computational Linguistics: NAACL 2022
The Chinese Causative-Passive Homonymy Disambiguation: an adversarial Dataset for NLI and a Probing Task — 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference
The Dangers of Underclaiming: Reasons for Caution When Reporting How NLP Systems Fail — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
The Grammar-Learning Trajectories of Neural Language Models — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
The better your Syntax, the better your Semantics? Probing Pretrained Language Models for the English Comparative Correlative — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Towards Better Characterization of Paraphrases — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Training Dynamics for Curriculum Learning: A Study on Monolingual and Cross-lingual NLU — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Uncovering More Shallow Heuristics: Probing the Natural Language Inference Capacities of Transformer-Based Pre-Trained Language Models Using Syllogistic Patterns — 2022 · arXiv:2201.07614
Unveiling Transformers with LEGO: a synthetic reasoning task — 2022 · arXiv:2206.04301
Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model — 2022 · arXiv:2201.11990
VALUE: Understanding Dialect Disparity in NLU — our card · 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
ViNLI: A Vietnamese Corpus for Studies on Open-Domain Natural Language Inference — 2022 · Proceedings of the 29th International Conference on Computational Linguistics
WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation — 2022 · arXiv:2201.05955
When Chosen Wisely, More Data Is What You Need: A Universal Sample-Efficient Strategy For Data Augmentation — 2022 · Findings of the Association for Computational Linguistics: ACL 2022
When Does Syntax Mediate Neural Language Model Performance? Evidence from Dropout Probes — 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systems — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Why is Winoground Hard? Investigating Failures in Visuolinguistic Compositionality — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Word Order Does Matter and Shuffled Language Models Know It — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Your fairness may vary: Pretrained language model fairness in toxic text classification — 2022 · Findings of the Association for Computational Linguistics: ACL 2022
\textitGeneralized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness — 2022 · Findings of the Association for Computational Linguistics: ACL 2022
``No, They Did Not'''': Dialogue Response Dynamics in Pre-trained Language Models — 2022 · Proceedings of the 29th International Conference on Computational Linguistics
``Will You Find These Shortcuts?'''' A Protocol for Evaluating the Faithfulness of Input Salience Methods for Text Classification — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
e-CARE: a New Dataset for Exploring Explainable Causal Reasoning — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
A Deep Metric Learning Approach to Account Linking — 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
A Logic-Based Framework for Natural Language Inference in Dutch — 2021 · arXiv:2110.03323
A Multilingual Benchmark for Probing Negation-Awareness with Minimal Pairs — 2021 · Proceedings of the 25th Conference on Computational Natural Language Learning
AESOP: Paraphrase Generation with Adaptive Syntactic Control — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Abstraction and analogy-making in artificial intelligence — 2021
Alignment Rationale for Natural Language Inference — 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
An Empirical Comparison of Instance Attribution Methods for NLP — 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
An Evaluation Dataset and Strategy for Building Robust Multi-turn Response Selection Model — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Annotation Difficulties in Natural Language Inference — 2021 · Proceedings of the 13th Brazilian Symposium in Information and Human Language Technology
Are Larger Pretrained Language Models Uniformly Better? Comparing Performance at the Instance Level — 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
Are NLP Models really able to Solve Simple Math Word Problems? — our card · 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Are Rotten Apples Edible? Challenging Commonsense Inference Ability with Exceptions — 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
Assessing the Generalization Capacity of Pre-trained Language Models through Japanese Adversarial Natural Language Inference — 2021 · Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP
Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Back to Square One: Artifact Detection, Training and Commonsense Disentanglement in the Winograd Schema — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing