lixiangnlp/c2e-mt-benchmark

Chinese-to-English Machine Translation Benchmark

Chinese-to-English Machine Translation Benchmark

Codes and pre-trained models for the Chinese-to-English machine translation benchmark.

Setup

Fistly, clone this repository and the related submodules:

git clone https://github.com/nusnlp/c2e-mt-benchmark.git
cd c2e-mt-benchmark
git submodule update --init --recursive

Secondly, go to each subdirectories under tools/* and follow the setup/installation instructions accordingly.

Finally, download and unpack the pre-trained models to the models/ subdirectory:

cd models/
wget http://sterling8.d2.comp.nus.edu.sg/~christian/c2e-mt-benchmark/pretrained.tar.gz
tar -xvzf pretrained.tar.gz
cd ..

Translating Text

The input is a plain text file containing Chinese sentences, one sentence per line. The input file is passed through the following pipeline:

1. Chinese word segmentation, by running scripts/segment.sh < input > input.seg
2. Translation (ensure that Theano flags are set as environment variables, replace nist with unpc for models trained on UN Parallel Corpus)
   • without re-ranking: scripts/translate-norerank.sh nist input.seg output [device(s)], where the device(s) include "gpu0", "gpu0 gpu1", or the default "cpu"
   • with re-ranking: scripts/translate-rerank.sh nist input.seg output [device(s)]
3. Recasing, by running scripts/recase.sh < output > output.rc
4. Detokenization, by running perl scripts/detokenizer.perl -l en < output.rc > output.detok

Test Set Translation Outputs

The outputs/ subdirectory contains the translation outputs produced by our models.

Scoreboard

The comparisons between the NIST test set results in BLEU achieved by our model and those achieved by prior published work are available here.

Publication

If you use the pre-trained models and settings from this repository, please cite the following paper:

Hadiwinoto, Christian and Ng, Hwee Tou (2018). Upping the ante: Towards a better benchmark for Chinese-to-English machine translation. To appear in Proceedings of the 11th edition of the Language Resources and Evaluation Conference. Miyazaki, Japan.