Dr Jaleh Delfani

This study explores the use of Google Translate (GT) for translating mental healthcare (MHealth) information and evaluates its accuracy, comprehensibility, and implications for multilingual healthcare communication through analysing GT output in the MHealth domain from English to Persian, Arabic, Turkish, Romanian, and Spanish. Two datasets comprising MHealth information from the UK National Health Service website and information leaflets from The Royal College of Psychiatrists were used. Native speakers of the target languages manually assessed the GT translations, focusing on medical terminology accuracy, comprehensibility, and critical syntactic/semantic errors. GT output analysis revealed challenges in accurately translating medical terminology, particularly in Arabic, Romanian, and Persian. Fluency issues were prevalent across various languages, affecting comprehension, mainly in Arabic and Spanish. Critical errors arose in specific contexts, such as bullet-point formatting, specifically in Persian, Turkish, and Romanian. Although improvements are seen in longer-text translations, there remains a need to enhance accuracy in medical and mental health terminology and fluency, whilst also addressing formatting issues for a more seamless user experience. The findings highlight the need to use customised translation engines for Mhealth translation and the challenges when relying solely on machine-translated medical content, emphasising the crucial role of human reviewers in multilingual healthcare communication.

Human beings find the process of narrative sequencing in written texts and moving imagery a relatively simple task. Key to the success of this activity is establishing coherence by using critical cues to identify key characters, objects, actions and locations as they contribute to plot development.

This paper reports on a user-experience study undertaken as part of the H2020 project MeMAD (‘Methods for Managing Audiovisual Data: Combining Automatic Efficiency with Human Accuracy’), in which multimedia content describers from the television and archive industries tested Flow, an online platform, designed to assist the post-editing of automatically generated data, in order to enhance the production of archival descriptions of film content. Our study captured the participant experience using screen recordings, the User Experience Questionnaire (UEQ), a benchmarked interactive media questionnaire and focus group discussions, reporting a broadly positive post-editing environment. Users designated the platform’s role in the collation of machine-generated content descriptions, transcripts, named-entities (location, persons, organisations) and translated text as helpful and likely to enhance creative outputs in the longer term. Suggestions for improving the platform included the addition of specialist vocabulary functionality, shot-type detection, film-topic labelling, and automatic music recognition. The limitations of the study are, most notably, the current level of accuracy achieved in computer vision outputs (i.e. automated video descriptions of film material) which has been hindered by the lack of reliable and accurate training data, and the need for a more narratively oriented interface which allows describers to develop their storytelling techniques and build descriptions which fit within a platform-hosted storyboarding functionality. While this work has value in its own right, it can also be regarded as paving the way for the future (semi)automation of audio descriptions to assist audiences experiencing sight impairment, cognitive accessibility difficulties or for whom ‘visionless’ multimedia consumption is their preferred option.