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

Advanced Computer Aided Translation with a Web-Based Workbench

We describe a web-based 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. It is available open source and integrates with multiple MT systems. The goal of the CASMACAT project 1 is to develop an advanced computer aided translation workbench. At the mid-point of the 3-year project, we release this tool as open source software. It already includes a wide range of novel advance

CASMACAT: A Computer-assisted Translation Workbench

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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. 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Simulating Spanish-English Code-Switching: El Modelo Está Generating Code-Switches

Multilingual speakers are able to switch from one language to the other (“code-switch”) between or within sentences. Because the underlying cognitive mechanisms are not well understood, in this study we use computational cognitive modeling to shed light on the process of code-switching. We employed the Bilingual Dual-path model, a Recurrent Neural Network of bilingual sentence production (Tsoukala et al., 2017), and simulated sentence production in simultaneous Spanish-English bilinguals. Our first goal was to investigate whether the model would code-switch without being exposed to code-switched training input. The model indeed produced code-switches even without any exposure to such input and the patterns of code-switches are in line with earlier linguistic work (Poplack,1980). The second goal of this study was to investigate an auxiliary phrase asymmetry that exists in Spanish-English code-switched production. Using this cognitive model, we examined a possible cause for this asymmetry. To our knowledge, this is the first computational cognitive model that aims to simulate code-switched sentence production