Who cites «Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework»

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

On Behalf of the Stakeholders: Trends in NLP Model Interpretability in the Era of LLMs — 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Understanding Common Ground Misalignment in Goal-Oriented Dialog: A Case-Study with Ubuntu Chat Logs — 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
CoELM: Construction-Enhanced Language Modeling — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task — 2023 · arXiv:2304.09138
Maximum Entropy Loss, the Silver Bullet Targeting Backdoor Attacks in Pre-trained Language Models — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
Multimodal Graph Transformer for Multimodal Question Answering — 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
What do Language Models know about word senses? Zero-Shot WSD with Language Models and Domain Inventories — 2023 · Proceedings of the 12th Global Wordnet Conference
When and Why Does Bias Mitigation Work? — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
ANLIzing the Adversarial Natural Language Inference Dataset — 2022 · Proceedings of the Society for Computation in Linguistics 2022
Curriculum: A Broad-Coverage Benchmark for Linguistic Phenomena in Natural Language Understanding — 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
On Measuring the Intrinsic Few-Shot Hardness of Datasets — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
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
Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning — 2022 · Findings of the Association for Computational Linguistics: NAACL 2022
Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference — 2022
Exploring Lexical Irregularities in Hypothesis-Only Models of Natural Language Inference — 2021 · arXiv:2101.07397
Extracting and filtering paraphrases by bridging natural language inference and paraphrasing — 2021 · arXiv:2111.07119
Figurative Language in Recognizing Textual Entailment — 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
Talking with the Theorem Prover to Interactively Solve Natural Language Inference — 2021 · Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation
Variation and generality in encoding of syntactic anomaly information in sentence embeddings — 2021 · Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP
A guide to the dataset explosion in QA, NLI, and commonsense reasoning — our card · 2020 · Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts
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
Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
ConjNLI: Natural Language Inference Over Conjunctive Sentences — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
End-to-End Bias Mitigation by Modelling Biases in Corpora — 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
Temporal Reasoning in Natural Language Inference — our card · 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
The Extraordinary Failure of Complement Coercion Crowdsourcing — 2020 · Proceedings of the First Workshop on Insights from Negative Results in NLP
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling — 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
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
Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction — 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Inherent Disagreements in Human Textual Inferences — our card · 2019
Probing Natural Language Inference Models through Semantic Fragments — 2019 · arXiv:1909.07521
Probing What Different NLP Tasks Teach Machines about Function Word Comprehension — 2019 · Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)
Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches — 2019 · arXiv:1904.01172
Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference — our card · 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
What do you learn from context? Probing for sentence structure in contextualized word representations — 2019 · arXiv:1905.06316
Behavior Analysis of NLI Models: Uncovering the Influence of Three Factors on Robustness — 2018 · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation — 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Lexicosyntactic Inference in Neural Models — 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
On the Evaluation of Semantic Phenomena in Neural Machine Translation Using Natural Language Inference — 2018 · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks — 2018 · arXiv:1811.01088
Transforming Question Answering Datasets Into Natural Language Inference Datasets — 2018 · arXiv:1809.02922