CASMACAT: An Open Source Workbench for Advanced Computer Aided Translation

Alabau, Vicent; Bonk, Ragnar; Buck, Christian; Carl, Michaël; Casacuberta, Francisco; García-Martínez, Mercedes; González, Jesús; Koehn, Philipp; Leiva, Luis A.; Mesa-Lao, Bartolomé; Ortiz, Daniel; Saint-Amand, Hervé; Sanchis, Germán; Tsoukala, Chara

doi:10.2478/pralin-2013-0016

Cited by 40 publications

(26 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The feedback signal can range from a simple word correction to complex, indeterministic ones, such as an eye-gaze tracking, combined with a handwritten correction, as in Alabau et al (2013). The features of the signal and its meaning determine the nature and behavior of the IMT system.…”

Section: Interactive Neural Machine Translationmentioning

confidence: 99%

Online learning for effort reduction in interactive neural machine translation

Peris

Casacuberta

2019

Computer Speech & Language

Self Cite

View full text Add to dashboard Cite

Neural machine translation systems require large amounts of training data and resources. Even with this, the quality of the translations may be insufficient for some users or domains. In such cases, the output of the system must be revised by a human agent. This can be done in a post-editing stage or following an interactive machine translation protocol.We explore the incremental update of neural machine translation systems during the post-editing or interactive translation processes. Such modifications aim to incorporate the new knowledge, from the edited sentences, into the translation system. Updates to the model are performed on-the-fly, as sentences are corrected, via online learning techniques. In addition, we implement a novel interactive, adaptive system, able to react to single-character interactions. This system greatly reduces the human effort required for obtaining high-quality translations.In order to stress our proposals, we conduct exhaustive experiments varying the amount and type of data available for training. Results show that online learning effectively achieves the objective of reducing the human effort required during the post-editing or the interactive machine translation stages. Moreover, these adaptive systems also perform well in scenarios with scarce resources. We show that a neural machine translation system can be rapidly adapted to a specific domain, exclusively by means of online learning techniques.3. We conduct a large experimentation, using public corpora containing different features from varied domains. We stress the translation systems, applying them in three different translation scenarios. 4. We show that OL brings significant improvements to the translation engines, in terms of translation quality and human effort reduction. Comparisons with other works in the literature also show that our adaptive, interactive systems are able to outperform the existing state-of-the-art. 5. We open-source all code developed in this work, in order to make research reproducible.The rest of this manuscript is structured as follows: the related work is reviewed in Section 2. Next, Section 3 briefly introduces the NMT technology, while the interactive protocol for NMT is presented in Section 4. OL is described and applied together with INMT in Section 5. Section 6 describes the experimental setup of this work. Results are presented and discussed in Section 7. Finally, we conclude the work and trace future lines of work in Section 8. Related workThis work puts together three thoroughly studied fields: neural machine translation, interactive machine translation and online learning. In this section, we briefly review the progress made in the last years in each one of these fields. Neural machine translationAlthough the first attempts of performing machine translation with neural networks date from long ago (Castaño and Casacuberta, 1997; Forcada andÑeco, 1997), NMT only took off recently. Kalchbrenner and Blunsom (2013) reintroduced full neural MT, although the results were non-competitive with re...

show abstract

Section: Interactive Neural Machine Translationmentioning

confidence: 99%

Online learning for effort reduction in interactive neural machine translation

Peris

Casacuberta

2019

Computer Speech & Language

Self Cite

View full text Add to dashboard Cite

show abstract

“…The main logging tool was the CASMACAT translators' workbench (Alabau, Bonk, et al 2013). This tool can be used as a translation environment with the additional advantage that it contains keystroke logging and mouse-tracking software suited for subsequent translation process research.…”

Section: Methodsmentioning

confidence: 99%

Translation Methods and Experience: A Comparative Analysis of Human Translation and Post-editing with Students and Professional Translators

Daems

Vandepitte

Hartsuiker

et al. 2017

meta

View full text Add to dashboard Cite

RÉSUMÉBien que l'on ait une certaine connaissance des avantages de la post-édition pour les textes techniques, il reste difficile de savoir si celle-ci est une solution de remplacement viable à la traduction humaine pour les textes généraux. Il faut par ailleurs comprendre davantage les deux méthodes de traduction, et la façon dont elles sont appliquées par les étudiants et les traducteurs professionnels, pour en déterminer les écueils et adapter la formation des traducteurs en conséquence. Dans cet article, nous cherchons à mieux cerner les différences entre la traduction humaine et la post-édition pour les articles de journaux. L'oculométrie et l'enregistrement de frappes ont été utilisés pour étudier le processus de traduction et dégager les gains en productivité, la charge cognitive et l'utilisation de ressources externes. Nous avons également examiné la qualité de la traduction et l'attitude des traducteurs à l'égard des deux méthodes de traduction. L'étude de ces différentes perspectives met en évidence que les deux groupes de traducteurs comme les deux méthodes de traduction sont plus proches que ce que l'on pouvait présumer. ABSTRACTWhile the benefits of using post-editing for technical texts have been more or less acknowledged, it remains unclear whether post-editing is a viable alternative to human translation for more general text types. In addition, we need a better understanding of both translation methods and how they are performed by students as well as professionals, so that pitfalls can be determined and translator training can be adapted accordingly. In this article, we aim to get a better understanding of the differences between human translation and post-editing for newspaper articles. Processes are registered by means of eye tracking and keystroke logging, which allows us to study translation speed, cognitive load, and the use of external resources. We also look at the final quality of the product as well as translators' attitude towards both methods of translation. Studying these different aspects shows that both methods and groups are more similar than anticipated. RESUMENMientras que se reconocen cada vez más los beneficios del uso de la post-edición para la traducción de textos técnicos, aún no existen pruebas de que la post-edición sea una alternativa viable a la traducción humana para los textos más generales. Además, necesitamos una mejor comprensión de ambos métodos de traducción, así como de la forma en que son realizados por estudiantes y por profesionales, para que se puedan definir los posibles obstáculos y adaptar la formación de los traductores en función de ellos. En este artículo, nuestro objetivo es lograr una mejor comprensión de las diferencias entre la traducción humana y la post-edición de artículos periodísticos. Los procesos de traducción se registran mediante el seguimiento ocular y el registro de pulsaciones de teclas, lo que nos permite estudiar la velocidad de traducción, la carga cognitiva y el uso de recursos externos. También analizamos la calidad final del pro...

show abstract

“…The results of user trials (Macklovitch, 2006) showed gains in productivity (measured in number of words translated per hour) of around 15-20%. A number of projects (Koehn, 2009;Ortiz-Martínez, 2011;Alabau et al, 2010Alabau et al, , 2013 have recently continued the research where TransType2 left it off. As regards tools, both Caitra (Koehn, 2009) and the CASMACAT Workbench (Alabau et al, 2013) are web applications with an underlying SMT-based ITP system.…”

Section: Related Workmentioning

confidence: 99%

“…A number of projects (Koehn, 2009;Ortiz-Martínez, 2011;Alabau et al, 2010Alabau et al, , 2013 have recently continued the research where TransType2 left it off. As regards tools, both Caitra (Koehn, 2009) and the CASMACAT Workbench (Alabau et al, 2013) are web applications with an underlying SMT-based ITP system. Caitra consults the internal translation table of the SMT system Moses (Koehn et al, 2007) in order to show the most likely translation options to the human translator.…”

Section: Related Workmentioning

confidence: 99%

An Open-Source Web-Based Tool for Resource-Agnostic Interactive Translation Prediction

Torregrosa¹,

Forcada²,

Pérez-Ortiz³

2014

The Prague Bulletin of Mathematical Linguistics

View full text Add to dashboard Cite

We present a web-based open-source tool for interactive translation prediction (ITP) and describe its underlying architecture. ITP systems assist human translators by making context-based computer-generated suggestions as they type. Most of the ITP systems in literature are strongly coupled with a statistical machine translation system that is conveniently adapted to provide the suggestions. Our system, however, follows a resource-agnostic approach and suggestions are obtained from any unmodified black-box bilingual resource. This paper reviews our ITP method and describes the architecture of Forecat, a web tool, partly based on the recent technology of web components, that eases the use of our ITP approach in any web application requiring this kind of translation assistance. We also evaluate the performance of our method when using an unmodified Moses-based statistical machine translation system as the bilingual resource.

show abstract

CASMACAT: An Open Source Workbench for Advanced Computer Aided Translation

Cited by 40 publications

References 5 publications

Online learning for effort reduction in interactive neural machine translation

Online learning for effort reduction in interactive neural machine translation

Translation Methods and Experience: A Comparative Analysis of Human Translation and Post-editing with Students and Professional Translators

An Open-Source Web-Based Tool for Resource-Agnostic Interactive Translation Prediction

Contact Info

Product

Resources

About