Second report on dissemination activities

Workpackage 7 comprises of dissemination activities of the casmacat project. In this report, we summarize the promotion of project goals, progress and outcomes to the larger academic research community, the commercial sector targeted by the work, and beyond

CASMACAT: An open source workbench for advanced computer aided translation

[EN] We describe an open source workbench that offers advanced computer aided translation (CAT) functionality: post-editing machine translation (MT), interactive translation prediction (ITP), visualization of word alignment, extensive logging with replay mode, integration with eye trackers and e-pen.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement 287576 (CASMACT). The workbench was developed in close collaboration with the MATECAT project.Alabau, V.; Bonkb, R.; Buck, C.; Carlb, M.; Casacuberta Nolla, F.; García-Martínez, M.; Gonzalez Rubio, J.... (2013). CASMACAT: An open source workbench for advanced computer aided translation. Prague Bulletin of Mathematical Linguistics. 100(1):101-112. https://doi.org/10.2478/pralin-2013-0016S1011121001Barrachina, S., Bender, O., Casacuberta, F., Civera, J., Cubel, E., Khadivi, S., … Vilar, J.-M. (2009). Statistical Approaches to Computer-Assisted Translation. Computational Linguistics, 35(1), 3-28. doi:10.1162/coli.2008.07-055-r2-06-2

Interactive translation prediction versus conventional post-editing in practice: a study with the CasMaCat workbench

[EN] We conducted a field trial in computer-assisted professional translation to compare interactive translation prediction (ITP) against conventional post-editing (PE) of machine translation (MT) output. In contrast to the conventional PE set-up, where an MT system first produces a static translation hypothesis that is then edited by a professional (hence "post-editing"), ITP constantly updates the translation hypothesis in real time in response to user edits. Our study involved nine professional translators and four reviewers working with the web-based CasMaCat workbench. Various new interactive features aiming to assist the post-editor/translator were also tested in this trial. Our results show that even with little training, ITP can be as productive as conventional PE in terms of the total time required to produce the final translation. Moreover, translation editors working with ITP require fewer key strokes to arrive at the final version of their translation.This work was supported by the European Union’s 7th Framework Programme (FP7/2007–2013) under grant agreement No 287576 (CasMaCat ).Sanchis Trilles, G.; Alabau, V.; Buck, C.; Carl, M.; Casacuberta Nolla, F.; Garcia Martinez, MM.; Germann, U.... (2014). Interactive translation prediction versus conventional post-editing in practice: a study with the CasMaCat workbench. Machine Translation. 28(3-4):217-235. https://doi.org/10.1007/s10590-014-9157-9S217235283-4Alabau V, Leiva LA, Ortiz-Martínez D, Casacuberta F (2012) User evaluation of interactive machine translation systems. In: Proceedings of the 16th Annual Conference of the European Association for Machine Translation, pp 20–23Alabau V, Buck C, Carl M, Casacuberta F, García-Martínez M, Germann U, González-Rubio J, Hill R, Koehn P, Leiva L, Mesa-Lao B, Ortiz-Martínez D, Saint-Amand H, Sanchis-Trilles G, Tsoukala C (2014) Casmacat: A computer-assisted translation workbench. In: Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, pp 25–28Alves F, Vale D (2009) Probing the unit of translation in time: aspects of the design and development of a web application for storing, annotating, and querying translation process data. Across Lang Cultures 10(2):251–273Bach N, Huang F, Al-Onaizan Y (2011) Goodness: A method for measuring machine translation confidence. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp 211–219Barrachina S, Bender O, Casacuberta F, Civera J, Cubel E, Khadivi S, Lagarda AL, Ney H, Tomás J, Vidal E, Vilar JM (2009) Statistical approaches to computer-assisted translation. Comput Linguist 35(1):3–28Brown PF, Della Pietra SA, Della Pietra VJ (1993) The mathematics of statistical machine translation: parameter estimation. Comput Linguist 19(2):263–311Callison-Burch C, Koehn P, Monz C, Post M, Soricut R, Specia L (2012) Findings of the 2012 workshop on statistical machine translation. In: Proceedings of the Seventh Workshop on Statistical Machine Translation, pp 10–51Carl M (2012a) The CRITT TPR-DB 1.0: A database for empirical human translation process research. In: Proceedings of the AMTA 2012 Workshop on Post-Editing Technology and Practice, pp 1–10Carl M (2012b) Translog-II: a program for recording user activity data for empirical reading and writing research. In: Proceedings of the Eighth International Conference on Language Resources and Evaluation, pp 4108–4112Carl M (2014) Produkt- und Prozesseinheiten in der CRITT Translation Process Research Database. In: Ahrens B (ed) Translationswissenschaftliches Kolloquium III: Beiträge zur Übersetzungs- und Dolmetschwissenschaft (Köln/Germersheim). Peter Lang, Frankfurt am Main, pp 247–266Carl M, Kay M (2011) Gazing and typing activities during translation : a comparative study of translation units of professional and student translators. Meta 56(4):952–975Doherty S, O’Brien S, Carl M (2010) Eye tracking as an MT evaluation technique. Mach Transl 24(1):1–13Elming J, Carl M, Balling LW (2014) Investigating user behaviour in post-editing and translation using the Casmacat workbench. In: O’Brien S, Winther Balling L, Carl M, Simard M, Specia L (eds) Post-editing of machine translation: processes and applications. Cambridge Scholar Publishing, Newcastle upon Tyne, pp 147–169Federico M, Cattelan A, Trombetti M (2012) Measuring user productivity in machine translation enhanced computer assisted translation. In: Proceedings of the Tenth Biennial Conference of the Association for Machine Translation in the AmericasFlournoy R, Duran C (2009) Machine translation and document localization at adobe: From pilot to production. In: Proceedings of MT Summit XIIGreen S, Heer J, Manning CD (2013) The efficacy of human post-editing for language translation. In: Proceedings of SIGCHI Conference on Human Factors in Computing Systems, pp 439–448Guerberof A (2009) Productivity and quality in mt post-editing. In: Proceedings of MT Summit XII-Workshop: Beyond Translation Memories: New Tools for Translators MTGuerberof A (2012) Productivity and quality in the post-editing of outputs from translation memories and machine translation. Ph.D. ThesisJust MA, Carpenter PA (1980) A theory of reading: from eye fixations to comprehension. Psychol Rev 87(4):329Koehn P (2009a) A process study of computer-aided translation. Mach Transl 23(4):241–263Koehn P (2009b) A web-based interactive computer aided translation tool. In: Proceedings of ACL-IJCNLP 2009 Software Demonstrations, pp 17–20Krings HP (2001) Repairing texts: empirical investigations of machine translation post-editing processes, vol 5. Kent State University Press, KentLacruz I, Shreve GM, Angelone E (2012) Average pause ratio as an indicator of cognitive effort in post-editing: a case study. In: Proceedings of the AMTA 2012 Workshop on Post-Editing Technology and Practice, pp 21–30Langlais P, Foster G, Lapalme G (2000) Transtype: A computer-aided translation typing system. In: Proceedings of the 2000 NAACL-ANLP Workshop on Embedded Machine Translation Systems, pp 46–51Leiva LA, Alabau V, Vidal E (2013) Error-proof, high-performance, and context-aware gestures for interactive text edition. In: Proceedings of the 2013 annual conference extended abstracts on Human factors in computing systems, pp 1227–1232Montgomery D (2004) Introduction to statistical quality control. Wiley, HobokenO’Brien S (2009) Eye tracking in translation process research: methodological challenges and solutions, Copenhagen Studies in Language, vol 38. Samfundslitteratur, Copenhagen, pp 251–266Ortiz-Martínez D, Casacuberta F (2014) The new Thot toolkit for fully automatic and interactive statistical machine translation. In: Proceedings of the 14th Annual Meeting of the European Association for Computational Linguistics: System Demonstrations, pp 45–48Plitt M, Masselot F (2010) A productivity test of statistical machine translation post-editing in a typical localisation context. Prague Bulletin Math Linguist 93(1):7–16Sanchis-Trilles G, Ortiz-Martínez D, Civera J, Casacuberta F, Vidal E, Hoang H (2008) Improving interactive machine translation via mouse actions. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp 485–494Simard M, Foster G (2013) Pepr: Post-edit propagation using phrase-based statistical machine translation. In: Proceedings of MT Summit XIV, pp 191–198Skadiņš R, Puriņš M, Skadiņa I, Vasiļjevs A (2011) Evaluation of SMT in localization to under-resourced inflected language. In: Proceedings of the 15th International Conference of the European Association for Machine Translation, pp 35–4

Error-proof, High-performance, and Context-aware Gestures for Interactive Text Edition

[EN] We present a straightforward solution to incorporate text-editing gestures to mixed-initiative user interfaces (MIUIs). Our approach provides (1) disambiguation from handwritten text, (2) edition context, (3) virtually perfect accuracy, and (4) a trivial implementation. An evaluation study with 32 e-pen users showed that our approach is suitable to production-ready environments. In addition, performance tests on a desktop PC and on a mobile device revealed that gestures are really fast to recognize (0.1 ms on average). Taken together, these results suggest that our approach can help developers to deploy simple but effective, high-performance text-editing gesturesThis work is supported by CasMaCat Project 287576 (FP7 ICT-2011.4.2)Leiva, LA.; Alabau, V.; Vidal, E. (2013). Error-proof, High-performance, and Context-aware Gestures for Interactive Text Edition. ACM. 1227-1232. http://hdl.handle.net/10251/179831S1227123

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

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. 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Multi-modal post-editing of machine translation

As MT quality continues to improve, more and more translators switch from traditional translation from scratch to PE of MT output, which has been shown to save time and reduce errors. Instead of mainly generating text, translators are now asked to correct errors within otherwise helpful translation proposals, where repetitive MT errors make the process tiresome, while hard-to-spot errors make PE a cognitively demanding activity. Our contribution is three-fold: first, we explore whether interaction modalities other than mouse and keyboard could well support PE by creating and testing the MMPE translation environment. MMPE allows translators to cross out or hand-write text, drag and drop words for reordering, use spoken commands or hand gestures to manipulate text, or to combine any of these input modalities. Second, our interviews revealed that translators see value in automatically receiving additional translation support when a high CL is detected during PE. We therefore developed a sensor framework using a wide range of physiological and behavioral data to estimate perceived CL and tested it in three studies, showing that multi-modal, eye, heart, and skin measures can be used to make translation environments cognition-aware. Third, we present two multi-encoder Transformer architectures for APE and discuss how these can adapt MT output to a domain and thereby avoid correcting repetitive MT errors.Angesichts der stetig steigenden Qualität maschineller Übersetzungssysteme (MÜ) post-editieren (PE) immer mehr Übersetzer die MÜ-Ausgabe, was im Vergleich zur herkömmlichen Übersetzung Zeit spart und Fehler reduziert. Anstatt primär Text zu generieren, müssen Übersetzer nun Fehler in ansonsten hilfreichen Übersetzungsvorschlägen korrigieren. Dennoch bleibt die Arbeit durch wiederkehrende MÜ-Fehler mühsam und schwer zu erkennende Fehler fordern die Übersetzer kognitiv. Wir tragen auf drei Ebenen zur Verbesserung des PE bei: Erstens untersuchen wir, ob andere Interaktionsmodalitäten als Maus und Tastatur das PE unterstützen können, indem wir die Übersetzungsumgebung MMPE entwickeln und testen. MMPE ermöglicht es, Text handschriftlich, per Sprache oder über Handgesten zu verändern, Wörter per Drag & Drop neu anzuordnen oder all diese Eingabemodalitäten zu kombinieren. Zweitens stellen wir ein Sensor-Framework vor, das eine Vielzahl physiologischer und verhaltensbezogener Messwerte verwendet, um die kognitive Last (KL) abzuschätzen. In drei Studien konnten wir zeigen, dass multimodale Messung von Augen-, Herz- und Hautmerkmalen verwendet werden kann, um Übersetzungsumgebungen an die KL der Übersetzer anzupassen. Drittens stellen wir zwei Multi-Encoder-Transformer-Architekturen für das automatische Post-Editieren (APE) vor und erörtern, wie diese die MÜ-Ausgabe an eine Domäne anpassen und dadurch die Korrektur von sich wiederholenden MÜ-Fehlern vermeiden können.Deutsche Forschungsgemeinschaft (DFG), Projekt MMP