JASS: Japanese-specific Sequence to Sequence Pre-training for Neural Machine Translation
This the repository for this paper.
Find extensions of this work and new pre-trained models here: code, paper
Requirements
Install OpenNMT-py (1.0) and subword-nmt.
pip install OpenNMT-py
pip install subword-nmt
Pre-trained JASS models
We release JASS models on 2 language pairs: ja+en, ja+ru. For Japanese seq2seq pretraining, we use our proposed JASS methods while MASS is utilized for English and Russian.
Model | Vocabulary | BPE codes |
---|---|---|
JASS-jaen | ja-en | ja-en.bpe.codes |
JASS-jaru | ja-ru | ja-ru.bpe.codes |
Usage
Run the bpe precrocessing for the dataset to be finetuned. After setting up the downloaded vocabulary for src and tgt sentences during the preprocessing phase by preprocess.py
of OpenNMT, use train_from
argument of train.py
in OpenNMT to implement the finetuning for the pretrained model.
Others
We will update the current Japanese--English pre-trained model and release pretrained models on Japanese--Chinese and Japanese--Korean. We released new models here: code
Reference
[1] Zhuoyuan Mao, Fabien Cromieres, Raj Dabre, Haiyue Song, Sadao Kurohashi, JASS: Japanese-specific Sequence to Sequence Pre-training for Neural Machine Translation
@inproceedings{mao-etal-2020-jass,
title = "{JASS}: {J}apanese-specific Sequence to Sequence Pre-training for Neural Machine Translation",
author = "Mao, Zhuoyuan and
Cromieres, Fabien and
Dabre, Raj and
Song, Haiyue and
Kurohashi, Sadao",
booktitle = "Proceedings of The 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://www.aclweb.org/anthology/2020.lrec-1.454",
pages = "3683--3691",
language = "English",
ISBN = "979-10-95546-34-4",
}