Awesome

charNgram2vec

This repository provieds the re-implemented code for pre-training character n-gram embeddings presented in our Joint Many-Task (JMT) paper [1]. Compared with the original single-thread code used in the paper, in the new version, substantial speedup is achieved (Not yet! Sorry). Some pre-trained character n-gram embeddings are also available at <a href="http://www.logos.t.u-tokyo.ac.jp/~hassy/publications/arxiv2016jmt/">my project page</a>.

This project requires a template library for linear algebra, Eigen (http://eigen.tuxfamily.org/index.php?title=Main_Page). Eigen 3.3.XX is recommended.

Usage

To use Eigen, please modify the line in Makefile as follows: EIGEN_LOCATION=$$HOME/local/eigen_3.3-beta1 # Modify here to use Eigen

More details will come soon!

Reference

[1] Kazuma Hashimoto, Caiming Xiong, Yoshimasa Tsuruoka, and Richard Socher. 2017. A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, <a href="https://arxiv.org/abs/1611.01587">arXiv cs.CL 1611.01587<a/>.

@InProceedings{hashimoto-jmt:2017:EMNLP2017,
  author    = {Hashimoto, Kazuma and Xiong, Caiming and Tsuruoka, Yoshimasa and Socher, Richard},
  title     = {{A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks}},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  note      = {To appear},
  url       = {http://arxiv.org/abs/1611.01587}
  }

Licence

MIT licence