Seminar (block) Machine Learning for Natural Language Processing (Winter 2018/19)

Lecturer: Thomas Trost and Dietrich Klakow

Location:  to be announced

Time: block course in the spring break 2019

HISPOS-Registration deadline: tba

Application for participation: closed!


  1. A. Maurer, M. Pontil, B. Romera-Paredes (2016). The benefit of multitask representation learning []
  2. A. Pentina, C. H. Lampert (2016). Multi-task learning with labeled and unlabeled tasks []
  3. M. Long, H. Zhu, J. Wang, M. I. Jordan (2016). Deep transfer learning with joint adaptation networks []
  4. Carl Doersch, Andrew Zisserman (2017). Multi-task Self-Supervised Visual Learning []
  5. Yanchao Yu, Arash Eshghi, Oliver Lemon (2017). Learning how to Learn: An Adaptive Dialogue Agent for Incrementally Learning Visually Grounded Word Meanings []
  6. Jesse Thomason, Jivko Sinapov, Raymond Mooney (2017). Guiding Interaction Behaviors for Multi-modal Grounded Language Learning []
  7. Siddharth Karamcheti, Edward Clem Williams, Dilip Arumugam, Mina Rhee, Nakul Gopalan, Lawson L.S. Wong, Stefanie Tellex (2017). A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions []
  8. Li Lucy, Jon Gauthier (2017). Are Distributional Representations Ready for the Real World? Evaluating Word Vectors for Grounded Perceptual Meaning []
  9. Ben Athiwaratkun, Andrew Gordon Wilson, Anima Anandkumar (2018). Probabilistic FastText for Multi-Sense Word Embeddings []
  10. Gail Weiss, Yoav Goldberg, Eran Yahav (2018). On the Practical Computational Power of Finite Precision RNNs for Language Recognition []
  11. Alan Akbik, Duncan Blythe, Roland Vollgraf (2018). Contextual String Embeddings for Sequence Labeling []
  12. Ben Krause, Emmanuel Kahembwe, Iain Murray, Steve Renals (2018). Dynamic Evaluation of Neural Sequence Models []
  13. Jiaqi Mu, Pramod Viswanath (2018). All-but-the-Top: Simple and Effective Postprocessing for Word Representations []
  14. Ben Athiwaratkun, Andrew Gordon Wilson (2018). Hierarchical Density Order Embeddings []
  15. Alex Nowak, David Folqué, Joan Bruna (2018). Divide and Conquer Networks []
  16. Kevin Tian, Teng Zhang, James Zou (2018). CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions []
  17. Zhilin Yang, Zihang Dai, Ruslan Salakhutdinov, William W. Cohen (2018). Breaking the Softmax Bottleneck: A High-Rank RNN Language Model []