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Human versus automatic quality evaluation of NMT and PBSMT

  • Published:
Machine Translation

Abstract

Neural machine translation (NMT) has recently gained substantial popularity not only in academia, but also in industry. For its acceptance in industry it is important to investigate how NMT performs in comparison to the phrase-based statistical MT (PBSMT) model, that until recently was the dominant MT paradigm. In the present work, we compare the quality of the PBSMT and NMT solutions of KantanMT—a commercial platform for custom MT—that are tailored to accommodate large-scale translation production, where there is a limited amount of time to train an end-to-end system (NMT or PBSMT). In order to satisfy the time requirements of our production line, we restrict the NMT training time to 4 days; to train a PBSMT system typically requires no longer than one day with the current training pipeline of KantanMT. To train both NMT and PBSMT engines for each language pair, we strictly use the same parallel corpora and the same pre- and post-processing steps (when applicable). Our results show that, even with time-restricted training of 4 days, NMT quality substantially surpasses that of PBSMT. Furthermore, we challenge the reliability of automatic quality evaluation metrics based on n-gram comparison (in particular F-measure, BLEU and TER) for NMT quality evaluation. We support our hypothesis with both analytical and empirical evidence. We investigate how suitable these metrics are when comparing the two different paradigms.

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Notes

  1. https://translate.google.com/.

  2. https://www.bing.com/translator/.

  3. https://systransoft.com.

  4. https://kantanmt.com/.

  5. F-measure, BLEU and TER are algorithms for quality evaluation of MT systems, typically used to estimate fluency, adequacy and extent of translation errors (cf. Way 2018b for more details).

  6. We consider tokenization and cleaning as general preprocessing steps; word segmentation (e.g. Byte Pair Encoding (BPE): Sennrich et al. 2016) is an NMT-specific pre-processing step.

  7. http://workshop2015.iwslt.org/.

  8. http://www.casmacat.eu/corpus/news-commentary.html.

  9. BLEU scores are presented in the range of 0–100.

  10. In http://kv-emptypages.blogspot.ie/2016/09/the-google-neural-machine-translation.html the author argues against the generalizability of the results and the appropriateness of the evaluations performed.

  11. http://tramooc.eu.

  12. Few translations will attain a score of 1 unless they are identical to a reference translation. Even translations by professional translators will not necessarily obtain a BLEU score of 1.

  13. Character-based F-measure was also shown to correlate well with human judgment (Popović 2015).

  14. See e.g. https://deeplearning4j.org/word2vec.html.

  15. https://github.com/moses-smt/mosesdecoder/scripts/generic/multi-bleu.perl.

  16. An MT engine refers to the package of models (translation, language and recasing models for PBSMT, and an encoder–decoder model for NMT) as well as to the required rules and dictionaries for pre- and post-processing.

  17. KantanMT provides both cloud-based and on-premise solutions.

  18. Typically additional monolingual data is employed to train the language model of a PBSMT engine. While we acknowledge that monolingual data, among other optimisations, may improve a PBSMT engine, within the scope of this work we keep the same-data assumption. That is, we do not employ any other data except for the parallel corpus provided. This requirement is vital when trying to answer the question: “When a user has at their disposal a set of parallel data, which of the two paradigms is preferable to train: PBSMT or NMT?”.

  19. Expressed in terms of increases in BLEU and F-measure and decreases in TER, as well as according to internal human evaluation.

  20. By Chinese, we mean Simplified Mandarin Chinese.

  21. https://ec.europa.eu/jrc/en/language-technologies/dgt-translation-memory.

  22. http://opus.lingfil.uu.se/.

  23. Training, testing and tuning data was normalised prior to building the MT engines.

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Acknowledgements

We would like to thank our external evaluators: Xiyi Fan, Ruopu Wang, Wan Nie, Ayumi Tanaka, Maki Iwamoto, Risako Hayakawa, Silvia Doehner, Daniela Naumann, Moritz Philipp, Annabella Ferola, Anna Ricciardelli, Paola Gentile, Celia Ruiz Arca, Clara Beltrá, Giulia Mattoni, as well as University College London, Dublin City University, KU Leuven, University of Strasbourg, and University of Stuttgart.

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Authors and Affiliations

  1. ADAPT Centre, School of Computing, Dublin City University, Dublin, Ireland

    Dimitar Shterionov & Andy Way

  2. KantanMT, Dublin City University, INVENT Building, Dublin, Ireland

    Riccardo Superbo, Pat Nagle, Laura Casanellas & Tony O’Dowd

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  1. Dimitar Shterionov
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  2. Riccardo Superbo
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  4. Laura Casanellas
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  5. Tony O’Dowd
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  6. Andy Way
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Correspondence to Dimitar Shterionov.

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Shterionov, D., Superbo, R., Nagle, P. et al. Human versus automatic quality evaluation of NMT and PBSMT. Machine Translation 32, 217–235 (2018). https://doi.org/10.1007/s10590-018-9220-z

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