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Repository created for sharing information among the Helsinki-NLP participants and interested users. Commenced by means of organizing the reading group sessions but intended for general purposes. Feel free to participate, add and modify information here.

Reading group

In a nutshell, on every session one of the participants will present 1 research article wich we will further discuss after the presentation.

We shall meet on a bi-weekly basis in the coffee room on the 6th floor of Metsätalo (unless otherwise stated).

For this to work smoothly, it is highly recommendable that all the attendants to a session had read the paper in turn.

Next session




Articles of Interest

New ideas and proposals are VERY WELCOME!

  • better undestanding the current state of the art for SNLI.


  • [1] Samuel R. Bowman et al. (2015) A large annotated corpus for learning natural language inference. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics.
  • [2] Hassan, H. et al. (2018). Achieving Human Parity on Automatic Chinese to English News Translation. In: In: e-print arXiv:1803.05567
  • [3] Johnson, M. et al. (2016). Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation. In: eprint arXiv:1611.04558
  • [4] Vaswani, A. et al. (2017). Attention is all you need. In: NIPS 2017
  • [5] Mikolov, T. et al. (2018). Advances in Pre-Training Distributed Word Representations. In: LREC 2018
  • [6] Grave, E. and Bojanowski, P. et al. (2018). Learning Word Vectors for 157 Languages. In: LREC 2018
  • [7] Li, C. et al. (2018). Measuring the Intrinsic Dimension of Objective Landscapes. In: ICLR 2018
  • [8] Lample, G. et al. (2018). Phrase-Based & Neural Unsupervised Machine Translation. In e-print arXiv:1804.07755
  • [9] Lu, Y. et. al (2018). A neural intelringua for multilingual machine translation. In: e-print arXiv:1804.08198v2
  • [10] Schwenk, H. and Douze, M.(2017). Learning Joint Multilingual Sentence Representations with Neural Machine Translation. In: e-print arXiv:1704.04154v2