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Article published In: Approaches to Machine Translation
Edited by Mahdieh Fakhar, Monica Vilhelm and Paz Díez-Arcón
[Translation and Translanguaging in Multilingual Contexts 11:1] 2025
► pp. 48–64

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Machine translation of tourism reviews

Quality assessment and localization

 | Universitat Politècnica de València
 | Universitat Politècnica de València
Published online: 7 January 2025
DOI logo with link
https://doi.org/10.1075/ttmc.00153.ros
Abstract
Machine translation (MT) has surpassed all quality expectations and its use has increased exponentially in recent years (Forcada, Mikel L. 2017. “Making Sense of Neural Machine Translation.” Translation Spaces 6(2): 291–309. Google Scholar logo with link to Google Scholar; Sánchez-Gijón, Pilar, Joss Moorkens, and Andy Way. 2019. “Post-Editing Neural Machine Translation Versus Translation Memory Segments.” Machine Translation 33(2): 31–59. Google Scholar logo with link to Google Scholar). One of the most frequent MT applications is the translation of user-generated content (UGC) and, more specifically, reviews on tourism portals such as Tripadvisor. Several authors agree that the degree of trust and credibility of a review, as the most important characteristics of UGC, depends largely on the perceived naturalness and authenticity of its writing (Pollach, Irene. 2006. “Electronic Word of Mouth: A Genre Analysis of Product Reviews on Consumer Opinion Web Sites.” In Proceedings of the 39th Hawaii International Conference on System Sciences, ed. by Ralph Sprague Jr., Vol. 31, 1530–1605. Washington D.C.: IEEE Computer Society. [URL]. ; Schemmann, Brita. 2011. “A Classification of Presentation Forms of Travel and Tourism-Related Online Consumer Reviews.” e-Review of Tourism Research 9 (2): 14–24. [URL]; . 2014. ““Usually Not One to Complain But…”: Constructing Identities in User-Generated Online Reviews.” In The Language of Social Media, ed. by Caroline Tagg, and Philip Seargeant, 65–90. London: Palgrave Macmillan. Google Scholar logo with link to Google Scholar). The review’s influence on the product’s reputation and on the purchase decision-making of future users has been fully demonstrated. Since review platforms make reviews available to users in different languages translated by MT, the quality of MT output should be studied from the point of view of the text’s adaptation to the requirements of a specific audience and market, following the principles elaborated in localization studies. The aim of this paper is to analyze the behavior of neural MT of user-generated content from the perspective of localization to check whether MT quality depends exclusively on linguistic or stylistic aspects or whether the aspects studied by localization, such as linguistic and cultural appropriateness for the target user, also play a decisive role. We compiled an English-Spanish parallel corpus consisting of 250 reviews retrieved from Tripadvisor. The reviews were originally written in English and MT processed into Spanish. Then the quality of the MT output was evaluated following two parameters: correctness and acceptance according to MT quality scales and localization guidelines.
Article outline
  • 1.Introduction
  • 2.Importance and characteristics of UGC
  • 3.Current status of MT: Characteristics of neural machine translation
    • 3.1Quality parameters and metrics
    • 3.2Machine translation and localization
  • 4.Methodology
  • 5.Analysis and discussion
    • 5.1Evaluation of the MT quality of reviews
    • 5.2Localization problems
      • 5.2.1Localization errors: Cultural variants
      • 5.2.2Localization errors: Linguistic variant
    • 5.3Other causes affecting quality
      • 5.3.1Errors caused by explanations or duplications in the original text
      • 5.3.2Errors caused by typos in the original text
    • 5.4Summary of results
  • 6.Conclusions
  • References
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Cited by (1)

Cited by one other publication

Chen, Shiyue & Yan Lin
2025. A multidimensional comparison of ChatGPT, Google Translate, and DeepL in Chinese tourism texts translation: fidelity, fluency, cultural sensitivity, and persuasiveness. Frontiers in Artificial Intelligence 8 DOI logo

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