Seminar (block) Machine Learning for Natural Language Processing

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: In order to participate in the seminar and for assigning the topics, please register here:

Deadline for registering is Tuesday  October 16th 23:59 Please also send a short e-mail to and explaining why the seminar is relevant to you. In case more than 12 students apply, we will use this text to select students. 

Please note that the registration system does not send any confirmation e-mails. Also don't forget to register in HISPOS.


  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 []