This is the official code used for WAT 2017 Description Paper titled A Bag of Useful Tricks for Practical Neural Machine Translation: Embedding Layer Initialization and Large Batch Size.
This program is based on Google's seq2seq implementation.
You may read the documentation of the original implementation at https://google.github.io/seq2seq/.
The following two features are added to the original implementation.
- Embedding Layer Initialization
- Ensemble of models
The example usage of our code are in ./examples
directory.
To create word embedding, please refer to ./data_preparation/init_vocab_w2v.sh
.
This script train word embeddings and convert them into .npy
format.
@InProceedings{neishi:WAT2017,
author = {Neishi, Masato and Sakuma, Jin and Tohda, Satoshi and Ishiwatari, Shonosuke and Yoshinaga, Naoki and Toyoda, Masashi},
title = {A Bag of Useful Tricks for Practical Neural Machine Translation: Embedding Layer Initialization and Large Batch Size},
booktitle = {Proceedings of the 4rd Workshop on Asian Translation (WAT2017)},
year = {2017 (to appear)}
}
The citation for Google's original paper.
@ARTICLE{Britz:2017,
author = {{Britz}, Denny and {Goldie}, Anna and {Luong}, Thang and {Le}, Quoc},
title = "{Massive Exploration of Neural Machine Translation Architectures}",
journal = {ArXiv e-prints},
archivePrefix = "arXiv",
eprinttype = {arxiv},
eprint = {1703.03906},
primaryClass = "cs.CL",
keywords = {Computer Science - Computation and Language},
year = 2017,
month = mar,
}