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..
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
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
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}
}