PIQA：Reasoning about physical commonsense in natural language

时间 2021-01-11

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2019.11 AAAI 2020 论文数据集在本文中，我们介绍了物理常识推理的任务以及相应的基准数据集“物理交互：问答”或“ PIQA”。尽管人们发现数据集很容易（准确度达到95％），但大型的预训练模型仍然很困难（〜77％）。我们提供有关现有模型缺乏的知识维度的分析，这为将来的研究提供了重要的机会。在孩子学习语言之前，他们开始根据周围物体的物理特性来形成类别和概念。这个模型的世界随着他

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1. PIQA: Reasoning about Physical Commonsense in Natural Language 笔记+翻译
2. Watson Natural Language Understanding
3. Natural Language Processing With Python (3)
4. CS224d: Deep Learning for Natural Language Process
5. 【Natural Language Processing】语言模型(Language Modeling)
6. Natural Language Processing With Python (1)
7. Natural Language Processing[论文合集]
8. Natural Language Processing With Python (2)
9. Natural Language Processing with Python - Chapter 0
10. Enhanced LSTM for Natural Language Inference
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1. PIQA: Reasoning about Physical Commonsense in Natural Language 笔记+翻译
2. Watson Natural Language Understanding
3. Natural Language Processing With Python (3)
4. CS224d: Deep Learning for Natural Language Process
5. 【Natural Language Processing】语言模型(Language Modeling)
6. Natural Language Processing With Python (1)
7. Natural Language Processing[论文合集]
8. Natural Language Processing With Python (2)
9. Natural Language Processing with Python - Chapter 0
10. Enhanced LSTM for Natural Language Inference

>>更多相关文章<<