Who cites «Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference» · page 6

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

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)
When Choosing Plausible Alternatives, Clever Hans can be Clever — 2019 · Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing
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