Improving the translation environment for professional translators

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project

Smart Computer-Aided Translation Environment (SCATE): Highlights

We present the highlights of the now finished 4-year SCATE project. It was completed in February 2018 and funded by the Flemish Government IWT-SBO, project No. 130041

How translators work in real-life: SCATE observations

The SCATE (Smart Computer-Assisted Translation Environment) research project is a multidisciplinary co-operation with the objective of improving the translators’ efficiency and consistency through a better integration of existing translation technologies and exploitation of resources. One aspect of this project consisted of undertaking an empirical case study at translators’ workplaces to understand their context of work and how they use translation tools and linguistics resources in real-life settings. During a period of approximately 8 months (November 2014 – September 2015), a total number of 16 translators and terminologists were observed at their workplaces and 4 others were interviewed remotely. The field research took place in Belgium, Luxembourg and the Netherlands, and involved professionals working in different organizational settings: public institutions, language service providers and self-employment. Prior the field observations, we investigated the features of some of the translation environment tools and launched a survey among the translation professionals to get an update on their use of technologies and terminology resources. The results of this empirical study reveal information about the physical working environment, the type of technologies translators use in their daily work and how they use them, and their methods of acquiring domain-specific terminology. In this short presentation we will give an overview of the main findings of the study, with focus on the acquisition of domain knowledge and terminology, and show how SCATE research could tackle some of the problems.status: publishe

Translator's methods of acquiring domain-specific terminology. Information retrieval in terminology using lexical Knowledge Patterns

The SCATE (Smart Computer-Assisted Translation Environment) research project is a multidisciplinary co-operation with the objective of improving translators’ efficiency and consistency through a better integration of existing translation technologies and exploitation of resources. One aspect of this project was to investigate the process of terminology extraction by humans and to automate the process of terminology extraction from comparable corpora. The methods consist of three main tasks: (1) study of translators’ methods to acquire domain knowledge and terminology, (2) determining comparable corpora, and (3) automatic terminology extraction from comparable text. In this presentation we will focus on the results of task (1) which has been completed, and present the preliminary results of task (3). In task (1) we collected the data through an international survey and observations of 16 translators and terminologists at their workplaces across a period of 8 months in 2015. The study revealed information about translators’ web search behaviour and usage of online linguistic resources to solve terminological problems. Besides these, we have identified needs and shortcomings of different CAT tools regarding the terminology management component, integration with online databases and exchange of terminological data. In task (3) we identified two subtasks: monolingual term extraction and term linking (i.e., linking terms to their corresponding translation). For the term extraction, we applied a hybrid approach combining linguistic and statistical information (Macken et al., 2013). Subsequently, two different techniques were investigated to link the translation equivalents: probabilistic topic models as well as different neural network architectures. The best results were obtained with a neural network model. In order to evaluate the performance of the different modules, we created a gold standard for three different domains (heart failure, wind energy, corruption) in three different languages (English, French, Dutch). References Poly-GrETEL. Available online at https://clarin.eu/showcase/poly-gretel-search-engine-querying-syntactic-constructions-parallel-treebanks Macken, L., Lefever, E. & Hoste, V. (2013). TExSIS: Bilingual Terminology Extraction from Parallel Corpora Using Chunk-based Alignment. Terminology, 19 (1), 1-30. John Benjamins Publishing Company, Amsterdam, Netherlands. van den Bergh, Jan et al. (2015). Recommendations for Translation Environments to Improve Translators' workflows. Translating and the Computer 37. Asling. London. van der Lek-Ciudin, Iulianna, Tom Vanallemeersch and Ken de Wachter (2015). Contextual Inquiries at translators’ workplaces. In Proceedings of the 1st TAO-CAT, Angers 2015 TermWise: Resources for Specialised Language Use. Information available online at http://liir.cs.kuleuven.be/projects.php?project=177status: accepte

Intellingo: An Intelligible Translation Environment

Translation environments offer various translation aids to support professional translators. However, translation aids typically provide only limited justification for the translation suggestions they propose. In this paper we present Intellingo, a translation environment that explores intelligibility for translation aids, to enable more sensible usage of translation suggestions. We performed a comparative study between an intelligible version and a non-intelligible version of Intellingo. The results show that although adding intelligibility does not necessarily result in significant changes to the user experience, translators can better assess translation suggestions without a negative impact on their performance. Intelligibility is preferred by translators when the additional information it conveys benefits the translation process and when this information is not part of the translator’s readily available knowledge.no ISSNstatus: publishe

The SCATE Prototype: A Smart Computer-Aided Translation Environment

We present the SCATE prototype: A Smart Computer-Aided Translation Environment, developed in the SCATE research project. Its user interface displays translation suggestions coming from different resources, in an intelligible and interactive way. It contains carefully designed representations that show relevant context to clarify why certain suggestions are given. In addition, several relationships between the source and the suggestions are made explicit so the user understands how a suggestion can be used in order to select the most appropriate one. Well-designed interaction techniques are included that improve the efficiency of the user interface. The suggestions are generated through different web services, such as fuzzy matching based on a translation memory (TM), machine translation (MT) and terminology extraction. MT and TM are combined using a pre-translation mechanism. A lookup mechanism highlights terms in the source segment that are available with their translation equivalents in the bilingual glossary. This paper presents the interface and the underlying web services, and discusses preliminary evaluations of the interface and the pre-translation mechanism.status: publishe

SCATE – Smart Computer-Aided Translation Environment – Year 3 (/4)

Vandeghinste V., Vanallemeersch T., Augustinus L., Van Eynde F., Pelemans J., Verwimp L., Wambacq P., Heyman G., Moens M.-F., van der Lek-Ciudin I., Steurs F., Rigouts Terryn A., Lefever E., Tezcan A., Macken L., Coppers S., Van den Bergh J., Luyten K., Coninx K., ''SCATE – Smart Computer-Aided Translation Environment – Year 3 (/4)'', 20th annual conference of the European Association for Machine Translation - EAMT 2017, May 28-31, 2017, Prague, Czech Republic (accepted).status: publishe

Improving the Translation Environment for Professional Translators

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project.status: Published onlin