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Quality Expectations of Machine Translation

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Translation Quality Assessment

Part of the book series: Machine Translation: Technologies and Applications ((MATRA,volume 1))

Abstract

Machine Translation (MT) is being deployed for a range of use-cases by millions of people on a daily basis. There should, therefore, be no doubt as to the utility of MT. However, not everyone is convinced that MT can be useful, especially as a productivity enhancer for human translators. In this chapter, I address this issue, describing how MT is currently deployed, how its output is evaluated and how this could be enhanced, especially as MT quality itself improves. Central to these issues is the acceptance that there is no longer a single ‘gold standard’ measure of quality, such that the situation in which MT is deployed needs to be borne in mind, especially with respect to the expected ‘shelf-life’ of the translation itself.

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Notes

  1. 1.

    This concept is also applied to crowdsourced translation by Jiménez-Crespo in this volume.

  2. 2.

    https://www.gala-global.org/industry/industry-facts-and-data

  3. 3.

    https://www.taus.net/think-tank/news/press-release/size-machine-translation-market-is-250-million-taus-publishes-new-market-report

  4. 4.

    Technavio estimate that the MT market will grow at a CAGR rate of 23.53% during 2015–19 (http://www.slideshare.net/technavio/global-machine-translation-market-20152019)

  5. 5.

    https://googleblog.blogspot.ie/2012/04/breaking-down-language-barriersix-years.html

  6. 6.

    https://events.google.com/io2016/

  7. 7.

    https://www.quora.com/Is-Facebooks-machine-translation-MT-based-on-principles-common-to-other-statistical-MT-systems-or-is-it-somehow-different

  8. 8.

    https://www.bing.com/translator

  9. 9.

    https://www.kantanmt.com/

  10. 10.

    https://www.microsoft.com/en-us/translator/hub.aspx

  11. 11.

    In its original exposition in Papineni et al. (2002), the BLEU (“Bilingual Evaluation Understudy”) score for a document was a figure between 0 and 1, the higher the better indicator of the quality of the MT system being evaluated. Here, and more commonly used nowadays in the field, this score is multiplied by 100 so that ‘BLEU points’ can be used to indicate progress compared to some benchmark.

  12. 12.

    We omit a lengthy discussion here on ‘round trip’ translation as an evaluation method (but cf. footnote 25), as it has been demonstrated by Somers (2005) to be an untrusted means of MT evaluation. In Way (2013), I note that in order to show that MT is error-prone, “sites like Translation Party (http://www.translationparty.com/) have been set up to demonstrate that continuous use of ‘back translation’ – that is, start with (say) an English sentence, translate it into (say) French, translate that output back into English, ad nauseum – ends up with a string that differs markedly from that which you started out with”. I quickly show that such websites have the opposite effect, and observe that “It’s easy to show MT to be useless; it’s just as easy to show it to be useful, but some people don’t want to”.

  13. 13.

    Indeed, the results from the ALPAC evaluation demonstrated there to be considerable correlation between intelligibility and fidelity.

  14. 14.

    http://www.itl.nist.gov/iad/mig/tests/mt/2006/doc/mt06eval_official_results.html

  15. 15.

    Minimally, in an SMT system these would be the “translation model” inferred from the parallel data, which essentially suggests which target-language words and phrases might best be used to try to create a translation of the source string; and the “language model” inferred from large collections of monolingual data, and used to try to create the most likely target-language ordering of those suggested target words and phrases.

  16. 16.

    See Sect. 3.2.2 for discussion of document-level versus sentence-level MT evaluation.

  17. 17.

    METEOR rewards MT output composed of fewer chunks. Output containing bigram (or longer) matches compared to the reference translation is penalised less than that comprising unigram matches only.

  18. 18.

    Nonetheless, more recent papers (Agarwal and Lavie 2008; Farrús et al. 2012) have also demonstrated that BLEU correlates extremely well with human judgement of translation quality.

  19. 19.

    This was introduced to prevent systems from outputting very short target-language strings (such as “the”) but nonetheless obtaining a high score. Accordingly, the shorter the translation compared to the reference translation, the more punitive the brevity penalty.

  20. 20.

    See Popović (this volume) for a discussion of the evolution of diagnostic MT error typologies.

  21. 21.

    ‘Phrases’ in phrase-based SMT refer only to n-gram sequences, i.e. contiguous sequences of surface words, not to the linguistic “constituent” sense of the word.

  22. 22.

    The Workshop (now Conference) on Machine Translation runs annual competitive MT system evaluations for a range of tasks. See http://www.statmt.org/wmt17/ for the latest in the series.

  23. 23.

    Over the past 10 years or so, SMT system developers have been incorporating more and more linguistic features. It is interesting to ponder whether BLEU (and similar metrics) disadvantages such linguistically enhanced systems compared to ‘pure’ SMT engines, in much the same way as RBMT output was penalised compared to pure n-gram-based systems.

  24. 24.

    Note, however, that the NMT system of Luong and Manning (2015) was more than 5 BLEU points better than a range of SMT systems for English to German. This sort of difference in BLEU score is more like what we might expect given the huge improvements in quality noted by Bentivogli et al. (2016) in their study. In this regard, both Shterionov et al. (2018) and Way (2018) note that BLEU may be under-reporting the difference in quality seen when using NMT systems, with the former attempting to measure the level of under-reporting using a set of novel metrics.

  25. 25.

    Without further comment, we merely note here that the ‘round trip’ (or ‘back’) translation discredited by Somers (2005) – cf. footnote 12 – has been demonstrated to be very useful in NMT as a means of generating additional ‘synthetic’ parallel training material (e.g. Sennrich et al. 2016b).

  26. 26.

    The subfield of quality estimation (see Specia and Shah in this volume) attempts to predict whether a new source string will result in a good or bad translation. This is different from MT evaluation, where we have a reference translation to compare the MT hypothesis against post hoc.

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Acknowledgments

This work has been supported by the ADAPT Centre for Digital Content Technology which is funded under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund.

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

    Andy Way

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  1. ADAPT Centre/School of Applied Language and Intercultural Studies, Dublin City University, Dublin, Ireland

    Joss Moorkens

  2. ADAPT Centre/School of Computing, Dublin City University, Dublin, Ireland

    Sheila Castilho

  3. ADAPT Centre/School of Computing, Dublin City University, Dublin, Ireland

    Federico Gaspari

  4. School of Humanities and Languages, The University of New South Wales, Sydney, Australia

    Stephen Doherty

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Way, A. (2018). Quality Expectations of Machine Translation. In: Moorkens, J., Castilho, S., Gaspari, F., Doherty, S. (eds) Translation Quality Assessment. Machine Translation: Technologies and Applications, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-91241-7_8

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