Dangers of Machine Translation: The Need for Professionally Translated Anticipatory Guidance Resources for Limited English Proficiency Caregivers

P, Das; Kuznetsova, Anna; Zhu, Meng’ou; Milanaik, Ruth

doi:10.1177/0009922818809494

Cited by 16 publications

(45 citation statements)

References 6 publications

(7 reference statements)

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“…The results showed that 2% of Spanish translations and 8% of translations into Chinese were inaccurate and could potentially cause significant harm (p. 580). In a similar investigation, Das et al (2019) evaluate the use of MT for translating anticipatory guidance material given to parents (i.e., proactive advice on a child's health and development). They tested translations from English into twenty other languages and concluded that Spanish and Portuguese were the only languages where Google Translate produced 'mostly accurate' results (p. 248).…”

Section: Evaluation Of Mtmentioning

confidence: 99%

“…Chen et al (2017) assess just three sentences, for instance, and the simpler sentences among them do not necessarily concern diabetes or indeed a healthcare setting (p. 4). 8 In Khoong et al (2019) and Das et al (2019), the assessment involved back-translating the Google Translate output into English. This is a problematic method.…”

Section: Evaluation Of Mtmentioning

confidence: 99%

“…MT use in 'everyday' communication (see Nurminen, 2018) is an emerging research area and there is also a growing body of research on public service interpreting (also known as community interpreting) aimed at making health and legal services accessible across languages (e.g., Angelelli, 2008). Research in the medical (e.g., Das et al, 2019) and legal fields (e.g., Yates, 2006), however, is often undertaken in parallel with, and without the full benefit of, related research in translation studies (e.g., Braun, 2019;Kenny, 2019). This prompted us to investigate the literature on MT use cases from these areas.…”

Section: Introductionmentioning

confidence: 99%

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Understanding the societal impacts of machine translation: a critical review of the literature on medical and legal use cases

Vieira

O’Hagan

O’Sullivan

2020

Information, Communication & Society

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The ready availability of machine translation (MT) systems such as Google Translate has profoundly changed how society engages with multilingual communication practices. In addition to private use situations, this technology is now used to overcome language barriers in high-risk settings such as hospitals and courts. MT errors pose serious risks in environments like these, but there is little understanding of the nature of these risks and of the wider implications of using this technology. This article is the first structured study of the consequences of uninformed MT use in healthcare and law. Based on a critical literature review, the article presents a qualitative meta-analysis of official documents and published research on the use of MT in these two fields. Its findings prompt calls for action in three areas. First, the review shows that research on MT use in healthcare and law can often disregard the complexities of language and language translation. The article calls for cross-disciplinary research to address this gap by ensuring that a growing body of relevant knowledge in translation studies informs research conducted within the medical and legal sectors. Second, the review highlights a broad societal need for higher levels of awareness of the specific strengths and, crucially, of the limitations of MT. Finally, the article concludes that MT technology can in its current state exacerbate social inequalities and put certain communities of users at greater risk. We highlight this as a persistent issue that merits further attention from researchers and policymakers.

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Section: Evaluation Of Mtmentioning

confidence: 99%

Section: Evaluation Of Mtmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Understanding the societal impacts of machine translation: a critical review of the literature on medical and legal use cases

Vieira

O’Hagan

O’Sullivan

2020

Information, Communication & Society

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“…Das and colleagues tested Google Translate to translate the safety guidelines of the American Academy of Pediatrics (AAP) into the 20 most commonly spoken languages in the United States, including Russian [14]. The authors evaluated the accuracy of the Google Translate results by performing human back-translation and assessing its quality using a five-point rubric adapted from the American Translators Association.…”

Section: Machine Translation and Health Informationmentioning

confidence: 99%

Assessing Human Post-Editing Efforts to Compare the Performance of Three Machine Translation Engines for English to Russian Translation of Cochrane Plain Language Health Information: Results of a Randomised Comparison

et al. 2021

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Cochrane produces independent research to improve healthcare decisions. It translates its research summaries into different languages to enable wider access, relying largely on volunteers. Machine translation (MT) could facilitate efficiency in Cochrane’s low-resource environment. We compared three off-the-shelf machine translation engines (MTEs)—DeepL, Google Translate and Microsoft Translator—for Russian translations of Cochrane plain language summaries (PLSs) by assessing the quantitative human post-editing effort within an established translation workflow and quality assurance process. 30 PLSs each were pre-translated with one of the three MTEs. Ten volunteer translators post-edited nine randomly assigned PLSs each—three per MTE—in their usual translation system, Memsource. Two editors performed a second editing step. Memsource’s Machine Translation Quality Estimation (MTQE) feature provided an artificial intelligence (AI)-powered estimate of how much editing would be required for each PLS, and the analysis feature calculated the amount of human editing after each editing step. Google Translate performed the best with highest average quality estimates for its initial MT output, and the lowest amount of human post-editing. DeepL performed slightly worse, and Microsoft Translator worst. Future developments in MT research and the associated industry may change our results.

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“…Some research has been conducted on the use of MT for increasing accessibility to health information, although in their recent review, Dew et al (2018, 60) found that most publications on this topic involve pilot studies or evaluations of the feasibility of existing tools, often freely available online systems like Google Translate (e.g. Das et al 2019). Work has focused on text translation, although some projects, such as BabelDr (Bouillon et al 2017), have involved automatic speech translation systems to assist in communication between doctors and patients.…”

Section: Accessibility To Health and Safety Informationmentioning

confidence: 99%

Machine translation and fair access to information

Nurminen

Koponen

2020

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This article contributes to the discussion on fairness and ethics in MT by highlighting efforts that have been made to use MT for the humanitarian purpose of increasing access to information for groups that are underserved. The article provides an overview of example projects in which MT has been implemented for this purpose in three contexts: civic participation, public health and safety, and media and culture. In addition, the article examines some of the ethical issues surrounding efforts to use MT for accessibility, including issues of quality, acceptability, and the need to involve stakeholders in development.

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Dangers of Machine Translation: The Need for Professionally Translated Anticipatory Guidance Resources for Limited English Proficiency Caregivers

Cited by 16 publications

References 6 publications

Understanding the societal impacts of machine translation: a critical review of the literature on medical and legal use cases

Understanding the societal impacts of machine translation: a critical review of the literature on medical and legal use cases

Assessing Human Post-Editing Efforts to Compare the Performance of Three Machine Translation Engines for English to Russian Translation of Cochrane Plain Language Health Information: Results of a Randomised Comparison

Machine translation and fair access to information

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