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Description

Implementation and output data of "Self-Attentive Residual Decoder for Neural Machine Translation".

This work is based on the dl4mt-tutorial by Kyunghyun Cho et al..

Test files

The output files of the 3 reported systems: baseline NMT (dl4mt-tutorial), average embeddings, and attentive residual connections are included here.

  • en-zh: unCorpus subset (2000 sentences)
  • es-en: newstest2013
  • en-de: newstest2014

Visualization:

We include visualization of the alignment matrix and the attention over previous words. For this purpose, use the following command:

python plot.py [source file] [target file] [sentence number]

Example:

python plot.py data/es-en/newstest2013.es data/es-en/attentive_newstest2013.en 1

Reference:

Miculicich, L., Pappas, N., Ram, D., & Popescu-Belis, A. (2018). Self-Attentive Residual Decoder for Neural Machine Translation. NAACL-HLT 2018

@inproceedings{werlenself,
  title={Self-Attentive Residual Decoder for Neural Machine Translation},
  author={Werlen, Lesly Miculicich and Pappas, Nikolaos and Ram, Dhananjay and Popescu-Belis, Andrei}
  booktitle={Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics}
  year={2018}
}

Contact:

lmiculicich@idiap.ch

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Implementation and output data of "Global-Context Neural Machine Translation through Target-Side Attentive Residual Connections"

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