Papers

Learn more about AI2's Lasting Impact Award
Viewing 531-540 of 991 papers
  • CLUE: A Chinese Language Understanding Evaluation Benchmark

    L. Xu, X.Zhang, L. Li, H. Hu, C. Cao, W. Liu, J. Li, Y. Li, K. Sun, Y. Xu, Y. Cui, C. Yu, Q. Dong, Y. Tian, D. Yu, B. Shi, J. Zeng, R. Wang, W. Xie, Y. Li, Y. Patterson, Z. Tian, Y. Zhang, H. Zhou, S. Liu, Q. Zhao, C. Yue, X. Zhang, Z. Yang, et.al.COLING 2020 We introduce CLUE, a Chinese Language Understanding Evaluation benchmark. It contains eight different tasks, including single-sentence classification, sentence pair classification, and machine reading comprehension. We evaluate CLUE on a number of existing…
  • Edited Media Understanding: Reasoning About Implications of Manipulated Images

    Jeff Da, Maxwell Forbes, Rowan Zellers, Anthony Zheng, Jena D. Hwang, Antoine Bosselut, Yejin ChoiarXiv2020 Multimodal disinformation, from `deepfakes' to simple edits that deceive, is an important societal problem. Yet at the same time, the vast majority of media edits are harmless -- such as a filtered vacation photo. The difference between this example, and…
  • Text mining approaches for dealing with the rapidly expanding literature on COVID-19

    Lucy Lu Wang, Kyle LoBriefings in Bioinformatics2020 More than 50 000 papers have been published about COVID-19 since the beginning of 2020 and several hundred new papers continue to be published every day. This incredible rate of scientific productivity leads to information overload, making it difficult for…
  • Belief Propagation Neural Networks

    J. Kuck, Shuvam Chakraborty, Hao Tang, R. Luo, Jiaming Song, A. Sabharwal, S. ErmonNeurIPS2020 Learned neural solvers have successfully been used to solve combinatorial optimization and decision problems. More general counting variants of these problems, however, are still largely solved with hand-crafted solvers. To bridge this gap, we introduce…
  • Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge

    Alon Talmor, Oyvind Tafjord, Peter Clark, Yoav Goldberg, Jonathan BerantNeurIPS • Spotlight Presentation2020 To what extent can a neural network systematically reason over symbolic facts? Evidence suggests that large pre-trained language models (LMs) acquire some reasoning capacity, but this ability is difficult to control. Recently, it has been shown that…
  • Learning About Objects by Learning to Interact with Them

    Martin Lohmann, Jordi Salvador, Aniruddha Kembhavi, Roozbeh Mottaghi NeurIPS2020 Much of the remarkable progress in computer vision has been focused around fully supervised learning mechanisms relying on highly curated datasets for a variety of tasks. In contrast, humans often learn about their world with little to no external supervision…
  • Do Neural Language Models Overcome Reporting Bias?

    Vered Shwartz and Yejin ChoiProceedings of the 28th International Conference on Computational Linguistics2020 Mining commonsense knowledge from corpora suffers from reporting bias, over-representing the rare at the expense of the trivial (Gordon and Van Durme, 2013). We study to what extent pre-trained language models overcome this issue. We find that while their…
  • From 'F' to 'A' on the N.Y. Regents Science Exams: An Overview of the Aristo Project

    Peter Clark, Oren Etzioni, Daniel Khashabi, Tushar Khot, Bhavana Dalvi Mishra, Kyle Richardson, Ashish Sabharwal, Carissa Schoenick, Oyvind Tafjord, Niket Tandon, Sumithra Bhakthavatsalam, Dirk Groeneveld, Michal Guerquin, Michael SchmitzAI Magazine2020
    AI2 Lasting Impact Award
    AI has achieved remarkable mastery over games such as Chess, Go, and Poker, and even Jeopardy!, but the rich variety of standardized exams has remained a landmark challenge. Even in 2016, the best AI system achieved merely 59.3% on an 8th Grade science exam…
  • Green AI

    Roy Schwartz, Jesse Dodge, Noah A. Smith, Oren EtzioniCACM2020 The computations required for deep learning research have been doubling every few months, resulting in an estimated 300,000x increase from 2012 to 2018 [2]. These computations have a surprisingly large carbon footprint [38]. Ironically, deep learning was…
  • Mitigating Biases in CORD-19 for Analyzing COVID-19 Literature

    Anshul Kanakia, Kuansan Wang, Yuxiao Dong, Boya Xie, Kyle Lo, Zhihong Shen, Lucy Lu Wang, Chiyuan Huang, Darrin Eide, Sebastian Kohlmeier, Chieh-Han WuFrontiers in Research Metrics and Analytics2020 On the behest of the Office of Science and Technology Policy in the White House, six institutions, including ours, have created an open research dataset called COVID-19 Research Dataset (CORD-19) to facilitate the development of question-answering systems…