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Neural Machine Translation Techniques for Named Entity Transliteration

This repository contains training scripts and instructions how to reproduce our systems submitted to the NEWS 2018 Shared Task on Transliteration of Named Entities, and described in R. Grundkiewicz, K. Heafield: Neural Machine Translation Techniques for Named Entity Transliteration, NEWS 2018, ACL 2018

Citation:

@InProceedings{grundkiewicz-heafield:2018:NEWS2018,
  author    = {Grundkiewicz, Roman  and  Heafield, Kenneth},
  title     = {Neural Machine Translation Techniques for Named Entity Transliteration},
  booktitle = {Proceedings of the Seventh Named Entities Workshop},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {89--94},
  url       = {http://www.aclweb.org/anthology/W18-2413}
}

Training

  1. Download and compile Marian in tools/marian-dev:

     cd tools
 git clone https://github.com/marian-nmt/marian-dev
 mkdir marian-dev/build
 cd marian-dev/build
 cmake .. -DCMAKE_BUILD_TYPE=Release
 make -j8
 cd ../../..

    If needed, please refer to the official Marian documentation at https://marian-nmt.github.io/docs

  2. Download data sets 01-04 from http://workshop.colips.org/news2018/dataset.html and unzip them into datasets.

  3. Prepare training and development data:

     cd experiments
 ./prepare-data.sh

  4. Train baseline systems specifying GPU device(s) and one or more language directions, e.g.:

     ./train.sh '0 1' EnVi EnCh ChEn

    Each system will be an ensemble of 4 deep RNN models rescored by 2 right-left models.

    The evaluation scores can be collected by running:

     ./show-results.sh

  5. A text file can be translated using the translate.sh script, for example:

     head data/EnVi.dev.src | ./translate.sh EnVi file.tmp 0 > file.out

  6. Prepare synthetic data with the back-translation or forward-translation method:

     ./prepare-synthetic-data.sh

    The systems can be re-trained with additional data by replacing original folders and re-running the training script, e.g.:

     mv data data.original
 mv synthetic data
 mv models models.baseline
 ./train.sh '0 1' EnVi EnCh ChEn
 ./show-results.sh

    For the EnVi system, this should display results similar to the following:

                                             ACC     Fscore  MRR     MAPref
 models.baseline/EnVi.1                  0.4680  0.8742  0.5582  0.4680
 models.baseline/EnVi.2                  0.4900  0.8806  0.5693  0.4900
 models.baseline/EnVi.3                  0.4580  0.8744  0.5521  0.4580
 models.baseline/EnVi.4                  0.4600  0.8692  0.5543  0.4600
 models.baseline/EnVi.ens                0.4740  0.8783  0.5649  0.4740
 models.baseline/EnVi.ens.r2l            0.4800  0.8815  0.5767  0.4800
 models.baseline/EnVi.ens.r2l.rescore    0.4880  0.8830  0.5777  0.4880
 models.baseline/EnVi.r2l.1              0.4520  0.8710  0.5548  0.4520
 models.baseline/EnVi.r2l.2              0.4860  0.8759  0.5791  0.4860
 models/EnVi.1                           0.4980  0.8856  0.5838  0.4980
 models/EnVi.2                           0.4860  0.8833  0.5771  0.4860
 models/EnVi.3                           0.4860  0.8836  0.5785  0.4860
 models/EnVi.4                           0.4980  0.8854  0.5833  0.4980
 models/EnVi.ens                         0.5000  0.8865  0.5859  0.5000
 models/EnVi.ens.r2l                     0.4820  0.8858  0.5817  0.4820
 models/EnVi.ens.r2l.rescore             0.5020  0.8884  0.5905  0.5020
 models/EnVi.r2l.1                       0.4800  0.8843  0.5789  0.4800
 models/EnVi.r2l.2                       0.4920  0.8876  0.5860  0.4920