Papers with MCTACO
“Going on a vacation” takes longer than “Going for a walk”: A Study of Temporal Commonsense Understanding (D19-1)
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| Challenge: | a new study examines temporal commonsense and compares it to human performance on a dataset . a previous study focused on duration, frequency, stationarity and ordering, but not all aspects of temporal similarity have been studied. |
| Approach: | They define five classes of temporal commonsense and use crowdsourcing to develop a new dataset that serves as a test set. |
| Outcome: | The proposed dataset shows that the best current methods are far behind human performance by 20%. |
Temporal Common Sense Acquisition with Minimal Supervision (2020.acl-main)
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| Challenge: | Temporal common sense is crucial for understanding natural language, but its acquisition is challenging . human annotation on such concepts is costly and often not made explicit in text . |
| Approach: | They propose a method that exploits explicit and implicit mentions of temporal common sense to build a temporal similarity language model. |
| Outcome: | The proposed model gives better predictions of various dimensions of temporal common sense than the standard BERT. |
Entropy-Aware Reshaping of Reinforcement Signals for Multi-Answer Reasoning (2026.findings-acl)
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| Challenge: | Reinforcement learning with verifiable rewards (RLVR) is a standard post-training paradigm for large language models. |
| Approach: | They propose a framework that reshapes how learning signals are normalized and aggregated. |
| Outcome: | Experiments on MCTACO and MMLU-Multi show that the proposed framework improves accuracy, training stability and cross-dataset transfer performance. |