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A review of Thai–English machine translation

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Machine Translation

Abstract

The improvement of machine translation (MT) for languages such as Thai requires access to knowledge reported in past and current research. With the distinctive features of several Asian languages as exhibited by Thai, and the recent change in focus of MT to a neural network-based approach, researchers require knowledge of understanding of these languages to aid further research. The purpose of this study is to provide an overview of the significant research in Thai–English MT that is both a valuable reference for current researchers in the field, as well as being suitable for the non-expert. We include details of the relevant language characteristics and extensive coverage of the important contributions to MT in Thailand. Although the application of neural networks to translation (called ‘neural machine translation’ (NMT)) is rapidly evolving and not widely reported in academic work involving Thai translation, it has shown potential for languages that require segmentation or have few resources and is therefore of special interest. Translation techniques are in many cases not directly applicable to many Asian languages because of their linguistic features and versatile writing systems, but NMT is already in widespread use in industry for the translation of Asian languages including Thai. Given this relevant success and potential for Thai translation, the aim is to integrate this important area into current literature on Thai–English MT, and to encourage interest and support advancement.

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Adapted from Haruechaiyasak et al. (2008)

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Notes

  1. Overview. History of the Thai language. https://www.thai-language.com/ref/Overview Accessed 14 June 2018.

  2. Inside a Thai Syllable: Part I. https://www.thai-language.com/id/830221 Accessed 7th July 2020.

  3. As Way and Hearne (2011, p.234) point out, “In seeking to understand SMT in particular, this is a key distinction; while the means by which RBMT and EBMT generate translations usually look somewhat plausible to linguists and translators, the methods of translation generation in SMT are not intuitively plausible. In fact, the methods used—at least until the recent attempts to incorporate syntactic knowledge into PB-SMT—are not intended to be either linguistically or cognitively plausible (just probabilistically plausible), and holding onto the notion that they somehow are or should be simply hinders understanding of SMT” (original emphasis).

  4. https://www.nist.gov/itl/iad/mig/openmt15-evaluation Accessed 14 June 2018.

  5. https://www.youtube.com/watch?v=nR74lBO5M3s&t Accessed 7 June 2018.

  6. https://www.cs.cmu.edu/~paisarn/software.html.

  7. https://sites.google.com/site/iwsltevaluation2015/mt-track Accessed 11 June 2018.

  8. https://sealang.net/thai/bitext.htm.

  9. Network-Based ASEAN Languages. https://www.aseanmt.org/index.php?q=index/status_update Accessed 25 June 2018.

  10. https://sites.google.com/site/iwsltevaluation2015/mt-track.

  11. https://cloud.google.com/translate/docs/languages#languages-nmt Accessed 7 June 2018.

  12. Microsoft Translator now offers more accurate and human-like translations of Thai. https://www.nationthailand.com/Startup_and_IT/30343651 Published on April 21, 2018, written by ThaiVisa. Accessed 6 June 2018.

  13. Baidu Thailand reveals marketing platform. https://www.bangkokpost.com/tech/local-news/1208193/baidu-thailand-reveals-marketing-platfor.

    Published on 3/3/2017, written by Srisamorn Phoosuphanusorn. Accessed 7 June 2018.

  14. South Korea's Naver launches official version of AI translator ‘Papago’. https://www.nationthailand.com/Startup_and_IT/30321483.

    Published on July 22, 2017, written by The Korea Herald/ANN. Accessed 9 December 2018.

  15. Explorations on Multi-lingual Neural Machine Translation. https://www.youtube.com/watch?v=dOewfz19dMA Accessed 7 June 2018.

  16. https://wordseg.readthedocs.io/en/latest/.

  17. https://pioneer.chula.ac.th/~awirote/resources/thai-word-segmentation.html Accessed 5 January 2019.

  18. cf. https://moin.delph-in.net/GlennSlayden Accessed 15th July 2020.

  19. Deepcut. A Thai Word Tokenization Library using Deep Neural Network. https://github.com/rkcosmos/deepcut Accessed 11 June 2018.

  20. Thai Word Segmentation with Bi-Directional RNN. https://github.com/sertiscorp/thai-word-segmentation Accessed 13 June 2018.

  21. Thai Word Segmentation with Bi-Directional RNN. https://www.sertiscorp.com/november-20-2017 Accessed 13 June 2018.

  22. Thai Natural Language Processing (Thai NLP) Resource. https://github.com/kobkrit/nlp_thai_resources Accessed 7th July 2020.

  23. CutKum. Thai Word-Segmentation with Deep Learning in Tensorflow. https://github.com/pucktada/cutkum Accessed 11 June 2018.

  24. https://github.com/KenjiroAI/SynThai Accessed 11 June 2018.

  25. https://www.ted.com/.

  26. https://opus.nlpl.eu/OpenSubtitles-v2018.php.

  27. https://www.opensubtitles.org/en/search/subs.

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    Séamus Lyons

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Lyons, S. A review of Thai–English machine translation. Machine Translation 34, 197–230 (2020). https://doi.org/10.1007/s10590-020-09248-8

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