Who cites «The CommitmentBank: Investigating projection in naturally occurring discourse» · page 3
234 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. ← the card
- CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP — 2021 · arXiv:2104.08835
- ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation — 2021 · arXiv:2107.02137
- Enhancing Transformers with Gradient Boosted Decision Trees for NLI Fine-Tuning — 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
- ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning — 2021 · arXiv:2111.10952
- Finetuned Language Models Are Zero-Shot Learners — 2021 · arXiv:2109.01652
- GLaM: Efficient Scaling of Language Models with Mixture-of-Experts — 2021 · arXiv:2112.06905
- GPT Understands, Too — 2021 · arXiv:2103.10385
- He Thinks He Knows Better than the Doctors: BERT for Event Factuality Fails on Pragmatics — our card · 2021
- How many data points is a prompt worth? — 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Identifying inherent disagreement in natural language inference — 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Issues with Entailment-based Zero-shot Text Classification — 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
- It''s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners — 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learning — 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021
- MetaICL: Learning to Learn In Context — 2021 · arXiv:2110.15943
- NOPE: A Corpus of Naturally-Occurring Presuppositions in English — our card · 2021 · Proceedings of the 25th Conference on Computational Natural Language Learning
- Surface Form Competition: Why the Highest Probability Answer Isn''t Always Right — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- The Power of Scale for Parameter-Efficient Prompt Tuning — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- True Few-Shot Learning with Language Models — 2021 · arXiv:2105.11447
- UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark — 2021 · arXiv:2103.13009
- WARP: Word-level Adversarial ReProgramming — 2021 · arXiv:2101.00121
- Want To Reduce Labeling Cost? GPT-3 Can Help — 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021
- AdapterFusion: Non-Destructive Task Composition for Transfer Learning — 2020 · arXiv:2005.00247 · Proceedings of EACL 2021
- 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
- DeBERTa: Decoding-enhanced BERT with Disentangled Attention — 2020 · arXiv:2006.03654
- Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work? — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Language Models are Few-Shot Learners — 2020 · arXiv:2005.14165
- New Protocols and Negative Results for Textual Entailment Data Collection — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Optimal Subarchitecture Extraction For BERT — 2020 · arXiv:2010.10499
- Which *BERT? A Survey Organizing Contextualized Encoders — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Do You Know That Florence Is Packed with Visitors? Evaluating State-of-the-art Models of Speaker Commitment — 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- 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)
- Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer — 2019 · arXiv:1910.10683
- Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches — 2019 · arXiv:1904.01172
- SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems — 2019 · arXiv:1905.00537