Who cites «Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning» · page 3
- K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters — 2020 · arXiv:2002.01808
- MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Measuring Massive Multitask Language Understanding — 2020 · arXiv:2009.03300
- MuTual: A Dataset for Multi-Turn Dialogue Reasoning — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Paragraph-level Commonsense Transformers with Recurrent Memory — 2020 · arXiv:2010.01486
- ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning — 2020 · arXiv:2002.04326
- Scene Restoring for Narrative Machine Reading Comprehension — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- The Box is in the Pen: Evaluating Commonsense Reasoning in Neural Machine Translation — 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
- To Test Machine Comprehension, Start by Defining Comprehension — 2020 · arXiv:2005.01525
- Towards Medical Machine Reading Comprehension with Structural Knowledge and Plain Text — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Towards Zero-Shot Conditional Summarization with Adaptive Multi-Task Fine-Tuning — 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
- XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- On Making Reading Comprehension More Comprehensive — 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering
- Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches — 2019 · arXiv:1904.01172
- adak-2024-text2afford-probing-object-affordance-prediction
- adiba-2022-unsupervised-domain-adaptation-question-answering
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- bansal-2023-few-shot-unified-question-answering
- bezancon-2023-spqr-deft2023-similarite-sorbonne-pour
- bruyn-2022-20q-overlap-free-world-knowledge
- burg-2025-aligning-black-box-language-models
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- do-2023-machine-reading-comprehension-vietnamese-customer
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- fu-2024-meta-rtl-reinforcement-based-meta
- gu-2024-digital-socrates-evaluating-llms-through
- hao-2026-bizcompass-benchmarking-reasoning-capabilities-llms
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- huang-2023-learning-easily-updated-general-purpose
- jung-2024-is-prompt-transfer-always-effective
- kalbaliyev-2024-narrative-question-answering-skills
- keith-2020-uncertainty-over-uncertainty-investigating-assumptions
- khanmohammadi-2025-calibrating-llm-confidence-probing-perturbed
- kohli-2025-groundcocoa-benchmark-evaluating-compositional-conditional
- li-2024-mcgill-nlp-group-submission-mrl
- li-2025-glider-global-local-instruction-driven
- lu-2025-piper-benchmarking-prompting-event-reasoning
- mishra-2024-amazutah-nlp-semeval-2024-task
- negheimish-2021-discrete-reasoning-templates-natural-language
- omura-2022-improving-commonsense-contingent-reasoning-pseudo
- pang-2023-sharpt-shared-latent-space-prompt
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- sun-2022-improving-machine-reading-comprehension-contextualized
- teo-2020-tr-semeval-2020-task-4
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- yang-2022-are-visual-linguistic-models-commonsense
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