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.

In emergency care settings, there is a crucial need for automated translation tools. We focus here on the BabelDr system, a speech-enabled fixed-phrase translator used to improve communication in emergency settings between doctors and allophone patients. The aim of the chapter is two-fold. First, we will assess if a bidirectional version of the phraselator allowing patients to answer doctors’ questions by selecting pictures from open-source databases will improve user satisfaction. Second, we wish to evaluate pictograph usability in this context. Our hypotheses are that images will in fact help to improve patient satisfaction and that multiple factors influence pictograph usability. Factors of interest include not only the comprehensibility of the pictographs per se, but also how the images are presented to the user with respect to their number and ordering. We showed that most respondents prefer to use the interface with pictographs and that multiple factors influence participants’ ability to find a pictograph based on a written form, but that the comprehensibility of the individual pictographs is probably the most important.

This chapter explains significant speech and translation technologies for healthcare professionals. We first examine the progress of automatic speech recognition (ASR) and text-to-speech (TTS). Turning to machine translation (MT), we briefly cover fixed-phrase-based translation systems (“phraselators”), with consideration of their advantages and disadvantages. The major types of full (wide-ranging, relatively unrestricted) MT – symbolic, statistical, and neural – are then explained in some detail. As an optional bonus, we provide an extended explanation of transformer-based neural translation. We postpone for a separate chapter discussion of practical applications in healthcare contexts of speech and translation technologies.

In this chapter, we review random encoding models that directly reduce the dimensionality of distributional data without first building a co-occurrence matrix. While matrix distributional semantic models (DSMs) output either explicit or implicit distributional vectors, random encoding models only produce low-dimensional embeddings, and emphasize efficiency, scalability, and incrementality in building distributional representations. We discuss the mathematical foundation for models based on random encoding, the Johnson-Lindenstrauss lemma. We introduce Random Projection, before turning to Random Indexing and BEAGLE, a random encoding model that encodes sequential information in distributional vectors. Then, we introduce a variant of Random Indexing that uses random permutations to represent the position of the context lexemes with respect to the target, similarly to BEAGLE. Finally, we discuss Self-Organizing Maps, a kind of unsupervised neural network that shares important similarities with random encoding models.

Distributional semantics is the study of how distributional information can be used to model semantic facts. Its theoretical foundation has become known as the Distributional Hypothesis: Lexemes with similar linguistic contexts have similar meanings. This chapter presents the epistemological principles of distributional semantics. First, we explore the historical roots of the Distributional Hypothesis, tracing them in several different theoretical traditions, including European structuralism, American distributionalism, the later philosophy of Ludwig Wittgenstein, corpus linguistics, and behaviorist and cognitive psychology. Then, we discuss the place of distributional semantics in theoretical and computational linguistics.

The most recent development in distributional semantics is represented by models based on artificial neural networks. In this chapter, we focus on the use of neural networks to build static embeddings. Like random encoding models, neural networks incrementally learn embeddings by reducing the high dimensionality of distributional data without building an explicit co-occurrence matrix. Differing from the first generation of distributional semantic models (DSMs), also termed count models, the distributional representations produced by neural DSMs are the by-product of training the network to predict neighboring words, hence the name of predict models. Since semantically similar words tend to co-occur with similar contexts, the network learns to encode similar lexemes with similar distributional vectors. After introducing the basic concepts of neural computation, we illustrate neural language models and their use to learn distributional representations. Then we pass to describe the most popular static neural DSMs, CBOW, and Skip-Gram. We conclude the chapter with a comparison between count and predict models.