The Impact of Visual Contextualization on UI Localization

[EN] Translating the text in an interface is a challenging task. Besides the jargon and technical terms, many of the strings are often very short, such as those shown in buttons and pull-down menus. Then, as a result of the lack of visual context in the traditional localization process, an important ambiguity problem arises. We study three approaches to solve this problem: using plain gettext (baseline condition), using gettext plus being able to operate the UI, and translating the UI in-place. We found that translators are substantially faster with plain gettext but commit a significantly higher number of errors in comparison to the other approaches. Unexpectedly, the mixed condition was slower and more error-prone than in-place translation. The latter was found to be comparable to plain gettext in terms of time, although some strings passed unnoticed as the UI was operated. Based on our results, we arrive at a set of recommendations to augment localization tools to improve translator's productivity.This work is supported by the 7th Framework Program of the European Commision (FP7/2007-13) under grant agreements 287576 (CASMACAT) and 600707 (tranScriptorium)Leiva, LA.; Alabau, V. (2014). The Impact of Visual Contextualization on UI Localization. ACM. 3739-3742. https://doi.org/10.1145/2556288.2556982S3739374

On String Prioritization in Web-based User Interface Localization

We have noticed that most of the current challenges affecting user interface localization could be easily approached if string prioritization would be made possible. In this paper, we tackle these challenges through Nimrod, a web-based internationalization tool that prioritizes user interface strings using a number of discriminative features. As a practical application, we investigate different prioritization strategies for different string categories from Wordpress, a popular open-source content management system with a large message catalog. Further, we contribute with WPLoc, a carefully annotated dataset so that others can reproduce our experiments and build upon this work. Strings in the WPLoc dataset are labeled as relevant and non-relevant, where relevant strings are in turn categorized as critical, informative, or navigational. Using state-of-the-art classifiers, we are able to retrieve strings in these categories with competitive accuracy. Nimrod and the WPLoc dataset are both publicly available for download.Leiva Torres, LA.; Alabau, V. (2014). On String Prioritization in Web-based User Interface Localization. Lecture Notes in Computer Science. 8787:460-473. doi:10.1007/978-3-319-11746-1_34S4604738787Breiman, L.: Bagging predictors. Machine Learning 24(2) (1996)Breiman, L.: Random forests. Machine Learning 45(1) (2001)Cascia, M.L., Sethi, S., Sclaro, S.: Combining textual and visual cues for content- based image retrieval on the world wide web. In: IEEEWorkshop on Content-Based Access of Image and Video Libraries, CBAIVL (1998)le Cessie, S., van Houwelingen, J.: Ridge estimators in logistic regression. Applied Statistics 41(1) (1992)Cleary, J.G., Trigg, L.E.: K*: An instance-based learner using an entropic distance measure. In: 12th International Conference on Machine Learning (1995)Collins, R.W.: Software localization for internet software: Issues and methods. IEEE Software 19(2) (2002)DePalma, D.A., Hegde, V., Pielmeier, H., Stewart, R.G.: The language services market. An annual review of the translation, localization, and interpreting services industry (2013), http://commonsenseadvisory.comDunne, K.J. (ed.): Perspectives on Localization. John Benjamins Publishing Company (2006)Esselink, B.: A Practical Guide to Localization. John Benjamins Publishing Company (2000)Gettext: The GNU gettext manual. version 0.18.2. (1995), http://www.gnu.org/Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: An update. SIGKDD Explorations 11(1) (2009)Hogan, J.M., Ho-Stuart, C., Pham, B.: Key challenges in software internationalisation. In: Workshop on Australasian Information Security, Data Mining and Web Intelligence, and Software Internationalisation (ACSW Frontiers) (2004)Keniston, K.: Software localization: Notes on technology and culture. Working Paper #26, Massachusetts Institute of Technology (1997)Leiva, L.A., Alabau, V.: An automatically generated interlanguage tailored to speakers of minority but culturally in uenced languages. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI) (2012)Leiva, L.A., Alabau, V.: The impact of visual contextualization on UI localization. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI) (2014)Reinecke, K., Bernstein, A.: Improving performance, perceived usability, and aesthetics with culturally adaptive user interfaces. ACM Transactions on Computer-Human Interaction (TOCHI) 18(2), 8:1–8:29 (2011)Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review 65(6) (1958)Sun, H.: Building a culturally-competent corporate web site: an exploratory study of cultural markers in multilingual web design. In: Proceedings of the 19th Annual International Conference on Computer Documentation (SIGDOC) (2001)De Troyer, O., Casteleyn, S.: Designing localized web sites. In: Zhou, X., Su, S., Papazoglou, M.P., Orlowska, M.E., Jeffery, K. (eds.) WISE 2004. LNCS, vol. 3306, pp. 547–558. Springer, Heidelberg (2004)VanReusel, J.F.: Five golden rules to achieve agile localization (2013), http://blogs.adobe.com/globalization/Wang, X., Zhang, L., Xie, T., Mei, H., Sun, J.: TranStrL: An automatic need-to- translate string locator for software internationlization. In: Proceedings of IEEE 31st International Conference on Software Engineering (ICSE) (2009

Understanding and improving subjective measures in human-computer interaction

In Human-Computer Interaction (HCI), research has shifted from a focus on usability and performance towards the holistic notion of User Experience (UX). Research into UX places special emphasis on concepts from psychology, such as emotion, trust, and motivation. Under this paradigm, elaborate methods to capture the richness and diversity of subjective experiences are needed. Although psychology offers a long-standing tradition of developing self-reported scales, it is currently undergoing radical changes in research and reporting practice. Hence, UX research is facing several challenges, such as the widespread use of ad-hoc questionnaires with unknown or unsatisfactory psychometric properties, or a lack of replication and transparency. Therefore, this thesis contributes to several gaps in the research by developing and validating self-reported scales in the domain of user motivation (manuscript 1), perceived user interface language quality (manuscript 2), and user trust (manuscript 3). Furthermore, issues of online research and practical considerations to ensure data quality are empirically examined (manuscript 4). Overall, this thesis provides well-documented templates for scale development, and may help improve scientific rigor in HCI