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Reasoning about events and tracking their influences is fundamental to understanding processes.
Fine-grained temporal relation extraction
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Exploiting structural and semantic context for commonsense knowledge base completion
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G-daug: Generative data augmentation for commonsense reasoning
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Unsupervised learning of narrative event chains
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Meteor 1.3: Automatic metric for reliable optimization and evaluation of machine translation systems
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
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Effective approaches to attention-based neural machine translation
Minh-Thang Luong, Hieu Pham, and Christopher D Manning. 2015 · 2015
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Script induction as language modeling
Rachel Rudinger, Pushpendre Rastogi, Francis Ferraro, and Benjamin Van Durme. 2015 · 2015
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Deep residual learning for image recognition
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Globally coherent text generation with neural checklist models
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
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Comet: Commonsense transformers for automatic knowledge graph construction
Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Çelikyilmaz, and Yejin Choi. 2019 · 2019
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Commonsense knowledge mining from pretrained models
Joe Davison, Joshua Feldman, and Alexander Rush. 2019 · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Language models as knowledge bases?
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Language models are unsupervised multitask learners
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Shikhar Sharma, Layla El Asri, Hannes Schulz, and Jeremie Zumer. 2017 · 2017
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Conceptnet 5.5: An open multilingual graph of general knowledge
Robyn Speer, Joshua Chin, and Catherine Havasi. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Simulating action dynamics with neural process networks
Antoine Bosselut, Corin Ennis, Omer Levy, Ari Holtzman, Dieter Fox, and Yejin Choi. 2018 · 2018
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Tracking state changes in procedural text: a challenge dataset and models for process paragraph comprehension
Bhavana Dalvi, Lifu Huang, Niket Tandon, Wen-tau Yih, and Peter Clark. 2018 · 2018
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Narrative modeling with memory chains and semantic supervision
Fei Liu, Trevor Cohn, and Timothy Baldwin. 2018 · 2018
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Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Atomic: An atlas of machine commonsense for if-then reasoning
Maarten Sap, Ronan Le Bras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A Smith, and Yejin Choi. 2019 · 2019
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Quarel: A dataset and models for answering questions about qualitative relationships
Oyvind Tafjord, Peter Clark, Matt Gardner, Wen-tau Yih, and Ashish Sabharwal. 2019 · 2019
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Wiqa: A dataset for “what if…” reasoning over procedural text
Niket Tandon, Bhavana Dalvi, Keisuke Sakaguchi, Peter Clark, and Antoine Bosselut. 2019 · 2019
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Open event extraction from online text using a generative adversarial network
Rui Wang, Deyu Zhou, and Yulan He. 2019 · 2019
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Exploring pre-trained language models for event extraction and generation
Sen Yang, Da wei Feng, Linbo Qiao, Zhigang Kan, and D. Li. 2019 · 2019
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Piqa: Reasoning about physical commonsense in natural language
Yonatan Bisk, Rowan Zellers, Ronan LeBras, Jianfeng Gao, and Yejin Choi. 2020 · 2020
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A knowledge-enhanced pretraining model for commonsense story generation
Jian Guan, Fei Huang, Zhihao Zhao, Xiaoyan Zhu, and Minlie Huang. 2020 · 2020
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