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

On the optimal decision rule for sequential interactive structured prediction

This is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters [Volume 33, Issue 16, 1 December 2012, Pages 2226–2231] DOI: 10.1016/j.patrec.2012.07.010[EN] Interactive structured prediction (ISP) is an emerging framework for structured prediction (SP) where the user and the system collaborate to produce a high quality output. Typically, search algorithms applied to ISP problems have been based on the algorithms for fully-automatic SP systems. However, the decision rule applied should not be considered as optimal since the goal in ISP is to reduce human effort instead of output errors. In this work, we present some insight into the theory of the sequential ISP search problem. First, it is formulated as a decision theory problem from which a general analytical formulation of the opti- mal decision rule is derived. Then, it is compared with the standard formulation to establish under what conditions the standard algorithm should perform similarly to the optimal decision rule. Finally, a general and practical implementation is given and evaluated against three classical ISP problems: interactive machine translation, interactive handwritten text recognition, and interactive speech recognition.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under Grant agreement no. 287576 (CasMaCat), and from the Spanish MEC/MICINN under the MIPRCV "Consolider Ingenio 2010" program (CSD2007-00018) and iTrans2 (TIN2009-14511) project. It is also supported by the Generalitat Valenciana under grant ALMPR (Prometeo/2009/01) and GV/2010/067. The authors thank the anonymous reviewers for their criticisms and suggestions.Alabau, V.; Sanchis Navarro, JA.; Casacuberta Nolla, F. (2012). On the optimal decision rule for sequential interactive structured prediction. Pattern Recognition Letters. 33(16):2226-2231. https://doi.org/10.1016/j.patrec.2012.07.010S22262231331

Contex-aware gestures for mixed-initiative text editings UIs

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Interacting with computers following peer review. The version of record is available online at: http://dx.doi.org/10.1093/iwc/iwu019[EN] This work is focused on enhancing highly interactive text-editing applications with gestures. Concretely, we study Computer Assisted Transcription of Text Images (CATTI), a handwriting transcription system that follows a corrective feedback paradigm, where both the user and the system collaborate efficiently to produce a high-quality text transcription. CATTI-like applications demand fast and accurate gesture recognition, for which we observed that current gesture recognizers are not adequate enough. In response to this need we developed MinGestures, a parametric context-aware gesture recognizer. Our contributions include a number of stroke features for disambiguating copy-mark gestures from handwritten text, plus the integration of these gestures in a CATTI application. It becomes finally possible to create highly interactive stroke-based text-editing interfaces, without worrying to verify the user intent on-screen. We performed a formal evaluation with 22 e-pen users and 32 mouse users using a gesture vocabulary of 10 symbols. MinGestures achieved an outstanding accuracy (<1% error rate) with very high performance (<1 ms of recognition time). We then integrated MinGestures in a CATTI prototype and tested the performance of the interactive handwriting system when it is driven by gestures. Our results show that using gestures in interactive handwriting applications is both advantageous and convenient when gestures are simple but context-aware. Taken together, this work suggests that text-editing interfaces not only can be easily augmented with simple gestures, but also may substantially improve user productivity.This work has been supported by the European Commission through the 7th Framework Program (tranScriptorium: FP7- ICT-2011-9, project 600707 and CasMaCat: FP7-ICT-2011-7, project 287576). It has also been supported by the Spanish MINECO under grant TIN2012-37475-C02-01 (STraDa), and the Generalitat Valenciana under grant ISIC/2012/004 (AMIIS).Leiva, LA.; Alabau, V.; Romero Gómez, V.; Toselli, AH.; Vidal, E. (2015). Contex-aware gestures for mixed-initiative text editings UIs. Interacting with Computers. 27(6):675-696. https://doi.org/10.1093/iwc/iwu019S675696276Alabau V. Leiva L. A. Transcribing Handwritten Text Images with a Word Soup Game. Proc. Extended Abstr. Hum. Factors Comput. Syst. (CHI EA) 2012.Alabau V. Rodríguez-Ruiz L. Sanchis A. Martínez-Gómez P. Casacuberta F. On Multimodal Interactive Machine Translation Using Speech Recognition. Proc. Int. Conf. Multimodal Interfaces (ICMI). 2011a.Alabau V. Sanchis A. Casacuberta F. Improving On-Line Handwritten Recognition using Translation Models in Multimodal Interactive Machine Translation. Proc. Assoc. Comput. Linguistics (ACL) 2011b.Alabau, V., Sanchis, A., & Casacuberta, F. (2014). Improving on-line handwritten recognition in interactive machine translation. Pattern Recognition, 47(3), 1217-1228. doi:10.1016/j.patcog.2013.09.035Anthony L. Wobbrock J. O. A Lightweight Multistroke Recognizer for User Interface Prototypes. Proc. Conf. Graph. Interface (GI). 2010.Anthony L. Wobbrock J. O. N-Protractor: a Fast and Accurate Multistroke Recognizer. Proc. Conf. Graph. Interface (GI) 2012.Anthony L. Vatavu R.-D. Wobbrock J. O. Understanding the Consistency of Users' Pen and Finger Stroke Gesture Articulation. Proc. Conf. Graph. Interface (GI). 2013.Appert C. Zhai S. Using Strokes as Command Shortcuts: Cognitive Benefits and Toolkit Support. Proc. SIGCHI Conf. Hum. Fact. Comput. Syst. (CHI) 2009.Bahlmann C. Haasdonk B. Burkhardt H. On-Line Handwriting Recognition with Support Vector Machines: A Kernel Approach. Proc. Int. Workshop Frontiers Handwriting Recognition (IWFHR). 2001.Bailly G. Lecolinet E. Nigay L. Flower Menus: a New Type of Marking Menu with Large Menu Breadth, within Groups and Efficient Expert Mode Memorization. Proc.Work. Conf. Adv. Vis. Interfaces (AVI) 2008.Balakrishnan R. Patel P. The PadMouse: Facilitating Selection and Spatial Positioning for the Non-Dominant Hand. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI). 1998.Bau O. Mackay W. E. Octopocus: A Dynamic Guide for Learning Gesture-Based Command Sets. Proc. ACM Symp. User Interface Softw. Technol. (UIST) 2008.Belaid A. Haton J. A syntactic approach for handwritten formula recognition. IEEE Trans. Pattern Anal. Mach. Intell. 1984;6:105-111.Bosch V. Bordes-Cabrera I. Munoz P. C. Hernández-Tornero C. Leiva L. A. Pastor M. Romero V. Toselli A. H. Vidal E. Transcribing a XVII Century Handwritten Botanical Specimen Book from Scratch. Proc. Int. Conf. Digital Access Textual Cultural Heritage (DATeCH). 2014.Buxton W. The natural language of interaction: a perspective on non-verbal dialogues. INFOR 1988;26:428-438.Cao X. Zhai S. Modeling Human Performance of Pen Stroke Gestures. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI). 2007.Castro-Bleda M. J. España-Boquera S. Llorens D. Marzal A. Prat F. Vilar J. M. Zamora-Martinez F. Speech Interaction in a Multimodal Tool for Handwritten Text Transcription. Proc. Int. Conf. Multimodal Interfaces (ICMI) 2011.Connell S. D. Jain A. K. Template-based on-line character recognition. Pattern Recognition 2000;34:1-14.Costagliola G. Deufemia V. Polese G. Risi M. A Parsing Technique for Sketch Recognition Systems. Proc. 2004 IEEE Symp. Vis. Lang. Hum. Centric Comput. (VLHCC). 2004.Culotta, A., Kristjansson, T., McCallum, A., & Viola, P. (2006). Corrective feedback and persistent learning for information extraction. Artificial Intelligence, 170(14-15), 1101-1122. doi:10.1016/j.artint.2006.08.001Deepu V. Madhvanath S. Ramakrishnan A. Principal Component Analysis for Online Handwritten Character Recognition. Proc. Int. Conf. Pattern Recognition (ICPR). 2004.Delaye A. Sekkal R. Anquetil E. Continuous Marking Menus for Learning Cursive Pen-Based Gestures. Proc. Int. Conf. Intell. User Interfaces (IUI) 2011.Dimitriadis Y. Coronado J. Towards an art-based mathematical editor that uses on-line handwritten symbol recognition. Pattern Recognition 1995;8:807-822.El Meseery M. El Din M. F. Mashali S. Fayek M. Darwish N. Sketch Recognition Using Particle Swarm Algorithms. Proc. 16th IEEE Int. Conf. Image Process. (ICIP). 2009.Goldberg D. Goodisman A. Stylus User Interfaces for Manipulating Text. Proc. ACM Symp. User Interface Softw. Technol. (UIST) 1991.Goldberg D. Richardson C. Touch-Typing with a Stylus. Proc. INTERCHI'93 Conf. Hum. Factors Comput. Syst. 1993.Stevens, M. E. (1968). Selected pattern recognition projects in Europe. Pattern Recognition, 1(2), 103-118. doi:10.1016/0031-3203(68)90002-2Hardock G. Design Issues for Line Driven Text Editing/ Annotation Systems. Proc. Conf. Graph. Interface (GI). 1991.Hardock G. Kurtenbach G. Buxton W. A Marking Based Interface for Collaborative Writing. Proc.ACM Symp. User Interface Softw. Technol. (UIST) 1993.Hinckley K. Baudisch P. Ramos G. Guimbretiere F. Design and Analysis of Delimiters for Selection-Action Pen Gesture Phrases in Scriboli. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI). 2005.Hong J. I. Landay J. A. SATIN: A Toolkit for Informal Ink-Based Applications. Proc. ACM Symp. User Interface Softw. Technol. (UIST) 2000.Horvitz E. Principles of Mixed-Initiative User Interfaces. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI). 1999.Huerst W. Yang J. Waibel A. Interactive Error Repair for an Online Handwriting Interface. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI) 2010.Jelinek F. Cambridge, Massachusetts: MIT Press; 1998. Statistical Methods for Speech Recognition.Johansson S. Atwell E. Garside R. Leech G. The Tagged LOB Corpus, User's Manual. Norwegian Computing Center for the Humanities. 1996.Karat C.-M. Halverson C. Horn D. Karat J. Patterns of Entry and Correction in Large Vocabulary Continuous Speech Recognition Systems. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI). 1999.Kerrick, D. D., & Bovik, A. C. (1988). Microprocessor-based recognition of handprinted characters from a tablet input. Pattern Recognition, 21(5), 525-537. doi:10.1016/0031-3203(88)90011-8Koschinski M. Winkler H. Lang M. Segmentation and Recognition of Symbols within Handwritten Mathematical Expressions. Proc. IEEE Int. Conf. Acoustics Speech Signal Process. (ICASSP). 1995.Kosmala A. Rigoll G. On-Line Handwritten Formula Recognition Using Statistical Methods. Proc. Int. Conf. Pattern Recognition (ICPR) 1998.Kristensson P. O. Discrete and continuous shape writing for text entry and control. 2007. Ph.D. Thesis, Linköping University, Sweden.Kristensson P. O. Denby L. C. Text Entry Performance of State of the Art Unconstrained Handwriting Recognition: a Longitudinal User Study. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI). 2009.Kristensson P. O. Denby L. C. Continuous Recognition and Visualization of Pen Strokes and Touch-Screen Gestures. Proc. Eighth Eurograph. Symp. Sketch-Based Interfaces Model. (SBIM) 2011.Kristensson P. O. Zhai S. SHARK2: A Large Vocabulary Shorthand Writing System for Pen-Based Computers. Proc. ACM Symp. User Interface Softw. Technol. (UIST). 2004.Kurtenbach G. P. The design and evaluation of marking menus. 1991. Ph.D. Thesis, University of Toronto.Kurtenbach G. P. Buxton W. Issues in Combining Marking and Direct Manipulation Techniques. Proc. ACM Symp. User Interface Softw. Technol. (UIST). 1991.Kurtenbach G. Buxton W. User Learning and Performance with Marking Menus. Proc. Extended Abstr. Hum. Factors Comput. Syst. (CHI EA) 1994.Kurtenbach, G., Sellen, A., & Buxton, W. (1993). An Empirical Evaluation of Some Articulatory and Cognitive Aspects of Marking Menus. Human-Computer Interaction, 8(1), 1-23. doi:10.1207/s15327051hci0801_1LaLomia M. User Acceptance of Handwritten Recognition Accuracy. Proc. Extended Abstr. Hum. Factors Comput. Syst. (CHI EA). 1994.Leiva L. A. Romero V. Toselli A. H. Vidal E. Evaluating an Interactive–Predictive Paradigm on Handwriting Transcription: A Case Study and Lessons Learned. Proc. 35th Annu. IEEE Comput. Softw. Appl. Conf. (COMPSAC) 2011.Leiva L. A. Alabau V. Vidal E. Error-Proof, High-Performance, and Context-Aware Gestures for Interactive Text Edition. Proc. Extended Abstr. Hum. Factors Comput. Syst. (CHI EA). 2013.Li Y. Protractor: A Fast and Accurate Gesture Recognizer. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI) 2010.Li W. Hammond T. Using Scribble Gestures to Enhance Editing Behaviors of Sketch Recognition Systems. Proc. Extended Abstr. Hum. Factors Comput. Syst. (CHI EA). 2012.Liao C. Guimbretière F. Hinckley K. Hollan J. Papiercraft: a gesture-based command system for interactive paper. ACM Trans. Comput.–Hum. Interaction (TOCHI) 2008;14:18:1-18:27.Liu P. Soong F. K. Word Graph Based Speech Rcognition Error Correction by Handwriting Input. Proc. Int. Conf. Multimodal Interfaces (ICMI). 2006.Long A. Landay J. Rowe L. Implications for a Gesture Design Tool. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI) 1999.Long A. C. Jr. Landay J. A. Rowe L. A. Michiels J. Visual Similarity of Pen Gestures. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI). 2000.MacKenzie, I. S., & Chang, L. (1999). A performance comparison of two handwriting recognizers. Interacting with Computers, 11(3), 283-297. doi:10.1016/s0953-5438(98)00030-7MacKenzie I. S. 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Beta release of CASMECAT workbench

This document contains details about the implementation of the 2nd prototype of the casmacat workbench and the Translation Process Research Database (TPR-DB). It outlines the major components of the workbench and their usage (Sections 1, 2, 3 and 6), as well as the structure and feature of the TPR-DB (Section 7). Since gaze information is the most valuable source for tracking translator e ort in text understanding, and due to the noise inherent in current head-free eye-tracking technology, Sections 4 and 5 report attempts to implement solutions for obtaining better gaze-to-word mapping accuracy. At the time of this writing, an installation guide1 has been written and made available to a select group of alpha testers (researchers from universities and research laboratories) to prepare a wider release of the prototype

Specification of CASMACAT workbench

This document contains details about the design of the casmacat workbench. It outlines the major components, their interaction, and gives also implementation guidelines. The deliverable is a snapshot of the document at the beginning of the casmacat project, it will be re ned throughout development and serves as technical documentation

Improving on-line handwritten recognition in interactive machine translation

[EN] On-line handwriting text recognition (HTR) could be used as a more natural way of interaction in many interactive applications. However, current HTR technology is far from developing error-free systems and, consequently, its use in many applications is limited. Despite this, there are many scenarios, as in the correction of the errors of fully-automatic systems using HTR in a post-editing step, in which the information from the specific task allows to constrain the search and therefore to improve the HTR accuracy. For example, in machine translation (MT), the on-line HTR system can also be used to correct translation errors. The HTR can take advantage of information from the translation problem such as the source sentence that is translated, the portion of the translated sentence that has been supervised by the human, or the translation error to be amended. Empirical experimentation suggests that this is a valuable information to improve the robustness of the on-line HTR system achieving remarkable results.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under Grant agreement no. 287576 (CasMaCat), from the EC (FEDER/FSE), and from the Spanish MEC/MICINN under the Active2Trans (TIN2012-31723) project. It is also supported by the Generalitat Valenciana under Grant ALMPR (Prometeo/2009/01) and GV/2010/067.Alabau Gonzalvo, V.; Sanchis Navarro, JA.; Casacuberta Nolla, F. (2014). Improving on-line handwritten recognition in interactive machine translation. Pattern Recognition. 47(3):1217-1228. https://doi.org/10.1016/j.patcog.2013.09.035S1217122847

Addendum

This document is an extension of D5.4 as suggested in the second review report. It contains de- tails about the implementation of the nal prototype of the casmacat workbench and outlines the improvements of the workbench with respect of the previous deliverable 5.4. The objective of WP5 is to integrate the translation system and user interface and to develop the casmacat workbench. This deliverable shows the functional components of the workbench and describes their interaction possibilities in the last casmacat prototype. It also describes the most recent additions to the workbench

Final release of the CASMACAT workbench

This document contains details about the implementation of the 3rd prototype of the casmacat workbench as well as the CRITT Translation Process Research Database (TPR-DB). It outlines the improvements of the workbench respect of the previous Deliverable 5.3. This deliverable will be updated in month 36 of the project with further improvements

An iterative multimodal framework for the transcription of handwritten historical documents

[EN] The transcription of historical documents is one of the most interesting tasks in which Handwritten Text Recognition can be applied, due to its interest in humanities research. One alternative for transcribing the ancient manuscripts is the use of speech dictation by using Automatic Speech Recognition techniques. In the two alternatives similar models (Hidden Markov Models and n-grams) and decoding processes (Viterbi decoding) are employed, which allows a possible combination of the two modalities with little diffi- culties. In this work, we explore the possibility of using recognition results of one modality to restrict the decoding process of the other modality, and apply this process iteratively. Results of these multimodal iterative alternatives are significantly better than the baseline uni-modal systems and better than the non-iterative alternatives. 2012 Elsevier B.V. All rights reserved.Work supported by the EC (FEDER/FSE) and the Spanish MEC/MICINN under the MIPRCV ’’Consolider Ingenio 2010’’ program (CSD2007-00018), iTrans2 (TIN2009–14511) and MITTRAL (TIN2009-14633-C03–01) projects. Also supported by the Spanish MITyC under the erudito.com (TSI-020110-2009-439) project and by the Generalitat Valenciana under grant GV/2010/067, and by the UPV under project PAID-05-11-2779 and grant UPV/2009/2851.Alabau, V.; Martínez Hinarejos, CD.; Romero Gómez, V.; Lagarda Arroyo, AL. (2014). An iterative multimodal framework for the transcription of handwritten historical documents. Pattern Recognition Letters. 35:195-203. https://doi.org/10.1016/j.patrec.2012.11.007S1952033

Analysis of the third field trial

In this work package, we evaluate the CasMaCat workbench in eld trials to study the use of the workbench in a real-world environment. We have also integrated the workbench into community translation platforms and collected user activity data from both eld trials and volunteer translators interacting with the workbench. This Deliverable covers Task 6.1 and 6.2. Task 6.1: Third eld trial at a translation agency (Celer Soluciones SL in Madrid) to evaluate the CasMaCat workbench in a real-world professional translation environment. Task 6.2: Analysis of translator feedback and activity data. Collection of feedback of translators' self-estimation through questionnaires and retrospective interviews. In addition to the originally planned third eld trial for 2014, we have also conducted an additional longitudinal study between April and May 2014 (as discussed in the last review meeting { December 2013)