Difference between revisions of "Fall 2017 CS595I Advanced NLP/ML Seminar"

Revision as of 22:29, 30 September 2017

Time: Tuesday 5-6pm. Location: HFH 1132.

If you registered this class, you should contact the instructor to lead the discussion of one paper below. If you don't lead the discussion, you will then need to write a 3-page final report in NIPS 2017 style, comparing any two of the papers below.

• 09/26:
  • Mahnaz Summer research presentation: Reinforced Pointer-Generator Network for Abstractive Summarization.
  • Xin: FeUdal Networks for Hierarchical Reinforcement Learning, Vezhnevets et al., ICML 2017 https://arxiv.org/pdf/1703.01161.pdf

• 10/03:
  • Vivek: The strange geometry of skip-gram with negative sampling, David Mimno and Laure Thompson, http://aclweb.org/anthology/D17-1307
  • Yijun: Latent Intention Dialogue Models, Wen et al., ICML 2017 https://arxiv.org/pdf/1705.10229.pdf

Word Embeddings

• Mahnaz--Dict2Vec: Learning Word Embeddings using Dictionaires, Julien Tissier and Christophe Gravier and Amaury Habrard http://aclweb.org/anthology/D17-1024
• Mimicking Word Embeddings using Subword RNNs, Yuval Pinter, Robert Guthrie and Jacob Eisenstein http://aclweb.org/anthology/D17-1010

Relational Learning and Reasoning

• Modeling Relational Data with Graph Convolutional Networks, Schlichtkrull et al., https://arxiv.org/pdf/1703.06103.pdf
• A simple neural network module for relational reasoning, Santoro et al., Arxiv https://arxiv.org/abs/1706.01427
• Adversarial Training for Relation Extraction, Yi Wu, David Bamman and Stuart Russell https://people.eecs.berkeley.edu/~russell/papers/emnlp17-relation.pdf
• Adversarial Examples for Evaluating Reading Comprehension Systems Robin Jia and Percy Liang https://arxiv.org/abs/1707.07328

Reinforcement Learning

• FeUdal Networks for Hierarchical Reinforcement Learning, Vezhnevets et al., ICML 2017 https://arxiv.org/pdf/1703.01161.pdf
• Deep reinforcement learning from human preferences, Christiano et al., https://arxiv.org/pdf/1706.03741.pdf
• Deep Reinforcement Learning that Matters, Henderson et al., arxiv https://arxiv.org/pdf/1709.06560.pdf
• Robust Imitation of Diverse Behaviors, Wang et al. 2017, https://arxiv.org/pdf/1707.02747.pdf
• Programmable Agents, Denil et al., https://arxiv.org/pdf/1706.06383v1.pdf
• Expected Policy Gradients, Kamil Ciosek, Shimon Whiteson, https://arxiv.org/abs/1706.05374
• Hindsight Experience Replay, Andrychowicz et al, https://arxiv.org/pdf/1707.01495.pdf
• Reinforcement Learning with Deep Energy-Based Policies Haarnoja et al, ICML 2017 http://proceedings.mlr.press/v70/haarnoja17a/haarnoja17a.pdf
• Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation, Kulkarni et al., 2016, https://arxiv.org/pdf/1604.06057.pdf
• Modular Multitask Reinforcement Learning with Policy Sketches, Andreas et al., ICML 2017 https://arxiv.org/pdf/1611.01796.pdf
• Device Placement Optimization with Reinforcement Learning, Azalia Mirhoseini et al. https://arxiv.org/pdf/1706.04972.pdf
• Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning, Gu et al., NIPS 2017 https://arxiv.org/abs/1706.00387
• Sequence Level Training with Recurrent Neural Networks https://arxiv.org/pdf/1511.06732.pdf

Learning (General)

• An Overview of Multi-Task Learning in Deep Neural Networks, Sebastian Ruder, https://arxiv.org/abs/1706.05098
• Understanding Black-box Predictions via Influence Functions, Koh and Liang, ICML 2017 Best Paper. https://arxiv.org/pdf/1703.04730.pdf
• The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process, Mei and Eisner, NIPS 2017 https://arxiv.org/pdf/1612.09328.pdf

Generation

• Generating Sentences by Editing Prototypes, Guu et al., arxiv https://arxiv.org/abs/1709.08878
• Multi-Task Video Captioning with Video and Entailment Generation, ACL 2017 Outstanding Paper https://arxiv.org/pdf/1704.07489.pdf
• Adversarial Feature Matching for Text Generation, Zhang et al., ICML 2017 http://proceedings.mlr.press/v70/zhang17b/zhang17b.pdf
• Adversarially Regularized Autoehttp://www.aclweb.org/anthology/D17-1120ncoders for Generating Discrete Structures, Zhao et al., https://arxiv.org/pdf/1706.04223.pdf

Dialog

• Reinforcement learning for spoken dialogue systems: Comparing strengths and weaknesses for practical deployment, DoD workshop, Interspeech 2006 https://pdfs.semanticscholar.org/b75b/e9388fefc44a20ceb40f6e0b916f0bbd7bbe.pdf
• Reinforcement learning for spoken dialogue systems, Singh et al., NIPS 2000 http://papers.nips.cc/paper/1775-reinforcement-learning-for-spoken-dialogue-systems.pdf
• Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management, Su et al., SIGDIAL 2017 https://arxiv.org/pdf/1707.00130.pdf
• Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning, Peng et al., EMNLP 2017. https://arxiv.org/abs/1704.03084
• Natural Language Does Not Emerge ‘Naturally’ in Multi-Agent Dialog, Kottur et al., EMNLP best paper, https://arxiv.org/pdf/1706.08502.pdf
• A Deep Reinforcement Learning Chatbot, Serban et al., https://arxiv.org/pdf/1709.02349.pdf
• Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses, ACL 2017 Outstanding Paper http://aclweb.org/anthology/P/P17/P17-1103.pdf

NLP for Computational Social Science

• Analyzing Language in Fake News and Political Fact-Checking, Hannah Rashkin, Eunsol Choi, Jin Yea Jang, Svitlana Volkova and Yejin Choi https://www.cs.jhu.edu/~svitlana/papers/RCYVC_EMNLP2017.pdf
• Human Centered NLP with User Factor Adaptation. Veronica Lynn, Youngseo Son, Vivek Kulkarni, Niranjan Balasubramanian, H Andrew Schwartz, http://www.aclweb.org/anthology/D17-1120