Google Translate Error Analysis for Mental Healthcare Information: Evaluating Accuracy, Comprehensibility, and Implications for Multilingual Healthcare Communication

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

Session 3. Audio description

Easy-to-read facilitates audio descriptions / Ulla Bohman (Boarve Konsult AB) ; The audio description of humour: an exploratory study / Graça Bigotte Chorão (Porto Polytechnic Institute) ; The sentient being's guide to automatic video description: a six-point roadmap for building the computer model of the future / Kim Starr (University of Surrey), Sabine Braun (University of Surrey), Jaleh Delfani (University of Surrey) ; Machine-assisted subtitling and audio description: experiences from a project and a look into the future / Maarit Koponen (University of Turku), Maija Hirvonen (Tampere University & University of Helsinki). Chair: Anna Jankowska (Jagiellonian University in Kraków

The translation of extralinguistic cultural references in animated feature films by unofficial subtitlers in Iran.

The preferred mode of audiovisual translation for foreign language programmes on state television and cinemas in Iran is dubbing. Dubbing is done by professionals who are supervised by the authorities, and a considerable part of foreign programmes is being censored. On the other hand, subtitling is not supervised by any formal institutions and is practiced by ‘unofficial’ subtitlers. Although their work does not necessarily follow subtitling norms, some of these subtitlers produce work of high-quality standards and their products are popular among the target audience. In order to shed light on the reason behind this popularity and address this under-researched phenomenon in the Iranian context, the current study focuses on the work of three informally recognised experienced subtitlers, whose works are popular among the audience, by taking animation as a case in point as a genre that has attracted dual audiences of (young) adults/children. The thesis contains a comparative analysis of the subtitles produced by the abovementioned unofficial subtitlers for five popular animated feature films to gauge the most frequently applied strategies by these subtitlers. As cultural elements have widely been recognised by scholars as one of the most challenging aspects of translation, Pedersen’s (2011) taxonomy of transfer strategies for Extralinguistic Cultural References (ECRs) in subtitling has been employed as a tool for analysing the subtitles. Pedersen’s model was adapted through partial redefinitions and extension of the categories to suit the purpose of the present study. The comparison focused on commonalities and differences in the subtitlers’ translation choices regarding the identified ECR instances in the selected animated feature films. The study reveals that unofficial subtitlers have a strong tendency to opt for target-oriented strategies when dealing with the translation of ECRs. Paraphrase was found to be the most frequently used strategy, followed by using a superordinate term and cultural substitution

Taking a Cue From the Human: Linguistic and Visual Prompts for the Automatic Sequencing of Multimodal Narrative

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

When Worlds Collide: AI-Created, Human-Mediated Video Description Services and the User Experience

| openaire: EC/H2020/780069/EU//MeMADThis 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.Peer reviewe

Work package 3: Barriers, needs and communication strategies

The first objective of this WP is to identify the most severe barriers to accessing mental healthcare services for LLP TCNs and to conduct an analysis of the communication, educational/training and practical needs arising for LLP TCNs and healthcare providers to promote this access. Building on this analysis, the second objective is to identify different communication strategies that can potentially mitigate the identified barriers and fulfil the identified needs effectively. Consideration will be given to (1) strategies addressing LLP TCNs’ and providers’ educational needs; (2) macro-strategies enabling access (e.g. use of a second language or lingua franca, individuals providing language support, translation tools); and (3) micro-strategies supporting effective communication and interaction within the afore-mentioned macro-options, in different communicative events and at different stages in the clinical care process. Given the increasing use of technology in healthcare settings as an emerging macro-strategy to access language support, especially automated translation such as Google Translate, our work on the second objective will include a small-scale simulated examination of the use of this technology in supporting access to mental healthcare services for LLP TCNs