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Language models (LMs) have demonstrated their capability in possessing commonsense knowledge of the physical world, a crucial aspect of performing tasks in everyday life.
Bleu: a Method for Automatic Evaluation of Machine Translation
Papineni, K.; Roukos, S.; Ward, T.; and Zhu, W.-J. 2002 · 2002
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Behavior Cloning for Autonomous Driving using Convolutional Neural Networks
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Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding
Yi, K.; Wu, J.; Gan, C.; Torralba, A.; Kohli, P.; and Tenenbaum, J. 2018 · 2018
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CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge
Talmor, A.; Herzig, J.; Lourie, N.; and Berant, J. 2019 · 2019
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VaTeX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research
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Brown, T. B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; Agarwal, S.; Herbert-Voss, A.; Krueger, G.; Henighan, T.; Child, R.; Ramesh, A.; Ziegler, D. M.; Wu, J.; Winter, C.; Hesse, C.; Chen, M.; Sigler, E.; Litwin, M.; Gray, S.; Chess, B.; Clark, J.; Berner, C.; McCandlish, S.; Radford, A.; Sutskever, I.; and Amodei, D. 2020 · 2020
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TabFact: A Large-scale Dataset for Table-based Fact Verification
Chen, W.; Wang, H.; Chen, J.; Zhang, Y.; Wang, H.; Li, S.; Zhou, X.; and Wang, W. Y. 2020 · 2020
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Herzig, J.; Nowak, P. K.; Müller, T.; Piccinno, F.; and Eisenschlos, J. 2020 · 2020
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BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
Lewis, M.; Liu, Y.; Goyal, N.; Ghazvininejad, M.; Mohamed, A.; Levy, O.; Stoyanov, V.; and Zettlemoyer, L. 2020 · 2020
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CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning
Lin, B. Y.; Zhou, W.; Shen, M.; Zhou, P.; Bhagavatula, C.; Choi, Y.; and Ren, X. 2020 · 2020
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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
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Episodic Transformer for Vision-and-Language Navigation
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ALFWorld: Aligning Text and Embodied Environments for Interactive Learning
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Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents
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FILM: Following Instructions in Language with Modular Methods
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Yin, P.; Neubig, G.; Yih, W.-t.; and Riedel, S. 2020 · 2020
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TabNet: Attentive Interpretable Tabular Learning
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Decision Transformer: Reinforcement Learning via Sequence Modeling
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Cited alongside, same era.
Liu, H.; Liu, Y.; He, H.; and Yang, H. 2022a
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TAPEX: Table Pre-training via Learning a Neural SQL Executor
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