Machine translation technology is having increasing applications in health and medical settings that involve communications and interactions between people from diverse language, cultural background. Machine translation tools offer low-cost, and accessible solutions to help close the gap in cross-lingual health communications. The risks of machine translation need to be effectively controlled and properly managed to boost the confidence in this developing technology among health professionals. This study integrates the methodological benefits of machine learning in machine translation quality evaluation, and more importantly, the prediction of clinically relevant machine translation errors based on the study of linguistic features of the English source texts.

Access to health-related information is vital in our society. Official health websites provide essential and beneficial information to the general population. In particular, they can represent a crucial public service when this information is fundamental to fight against a new health threat –such as the COVID-19 pandemic. Yet, for these websites to achieve their ultimate informative goal, they need to ensure that their content is accessible to all users, especially to people with disabilities. Many of these websites –especially those from institutions operating in multilingual countries – offer their content in several languages, which, by definition, is an accessibility best practice. However, the level of accessibility achieved might not always be the same in all the language versions available. In fact, previous studies focusing on other types of multilingual websites have shown that localized versions are usually less accessible than the original ones. In this chapter, we present a research study that involved the examination of seventy-four official multilingual health sites to understand the current situation in terms of accessibility compliance. In particular, the home pages in two languages – English, original version, and Spanish, localized version – were checked against two specific success criteria (SC) from the Web Content Accessibility Guidelines (WCAG) current standard, using both automatic and manual evaluation methods. We observed that although overall accessibility scores were similar, the localized pages obtained worse results in the two SC analyzed more in depth – that is, language and title of the page. We contend that this finding could be explained by a lack of accessibility awareness or knowledge of those participating in the localization process. We thus advocate the existence of web professionals with an interdisciplinary background that could create multilingual accessible sites, providing an inclusive web experience for all.

This chapter concludes the book with a general summary of the current state of distributional semantics, its future prospects, and challenges.