Group Reading F16

• 10/13:
  • Presenter: William Wang, Random Walk Inference and Learning in A Large Scale Knowledge Base, Ni Lao, Tom Mitchell, William W. Cohen. http://www.cs.cmu.edu/~nlao/publication/2011/2011.emnlp.paper.pdf
  • Presenter: Tyler Vuong, Translating Embeddings for Modeling Multi-relational Data Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran et al.,https://www.utc.fr/~bordesan/dokuwiki/_media/en/transe_nips13.pdf

• 10/20:
  • Presenter: Thien Hoang, Compositional Vector Space Models for Knowledge Base Completion. Arvind Neelakantan, Benjamin Roth and Andrew McCallum. https://people.cs.umass.edu/~arvind/acl2015.pdf
  • Presenter: Darren Huang, Relation Classification via Convolutional Deep Neural Network, Daojian Zeng, Kang Liu, Siwei Lai, Guangyou Zhou and Jun Zhao. http://www.aclweb.org/anthology/C14-1220

• 10/27:
  • Presenter: Peter Zhe Fu, GANs: Generative Adversarial Networks, Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio. https://arxiv.org/abs/1406.2661
  • Presenter: Shaoyi Zhang, SeqGAN: SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient, Lantao Yu, Weinan Zhang, Jun Wang, Yong Yu, https://arxiv.org/abs/1609.05473

• 11/3: No meeting. EMNLP + midterm.

• 11/10:
  • Presenter: Metehan Ozten: Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning, Karthik Narasimhan, Adam Yala, Regina Barzilay. EMNLP 2016. https://arxiv.org/pdf/1603.07954v3.pdf
  • Presenter: Ke Ni: Learning to compose neural networks for question answering, Jacob Andreas, Marcus Rohrbach, Trevor Darrell and Dan Klein. NAACL 2016.https://arxiv.org/abs/1601.01705
  • Some good EMNLP papers:
    • Rationalizing Neural Predictions, Tao Lei, Regina Barzilay, Tommi Jaakkola, https://arxiv.org/abs/1606.04155
    • Deep Reinforcement Learning for Dialogue Generation, Jiwei Li, Will Monroe, Alan Ritter, Michel Galley, Jianfeng Gao, Dan Jurafsky, https://arxiv.org/abs/1606.01541
    • Sequence-to-Sequence Learning as Beam-Search Optimization, Sam Wiseman and Alexander M. Rush, https://arxiv.org/pdf/1606.02960.pdf
    • Mining Inference Formulas by Goal-Directed Random Walks, Mining Inference Formulas by Goal-Directed Random Walks, https://www.aclweb.org/anthology/D/D16/D16-1145.pdf

• 11/17:
  • Presenter: Daniel Auto-Encoding Variational Bayes, Kingma and Welling, ICLR 2014, https://arxiv.org/pdf/1312.6114v10.pdf
  • NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING, ICLR 2017, http://openreview.net/pdf?id=r1Ue8Hcxg

• 11/24: Thanksgiving. No Meeting.