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Edited by Gloria Corpas Pastor and Isabel Durán-Muñoz

Trends in E-Tools and Resources for Translators and Interpreters offers a collection of contributions from key players in the field of translation and interpreting that accurately outline some of the most cutting-edge technologies in this field that are available or under development at the moment in both professional and academic contexts.
Particularly, this volume provides a wide picture of the state of the art, looking not only at the world of technology for translators but also at the hitherto overlooked world of technology for interpreters. This volume is accessible and comprehensive enough to be of benefit to different categories of readers: scholars, professionals and trainees.

Contributors are: Pierrette Bouillon, Gloria Corpas Pastor, Hernani Costa, Isabel Durán-Muñoz, Claudio Fantinuoli, Johanna Gerlach, Joanna Gough, Asheesh Gulati, Veronique Hoste, Amélie Josselin, David Lewis, Lieve Macken, John Moran, Aurelie Picton, Emmanuel Planas, Éric Poirier, Victoria Porro, Celia Rico Pérez, Christian Saam, Pilar Sánchez-Gijón, Míriam Seghiri Domínguez, Violeta Seretan, Arda Tezcan, Olga Torres, and Anna Zaretskaya.

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Joanna Gough

Abstract

The use of resources plays an important role in the translation process. Despite the increased adoption of translation technologies, professional translators can spend on average as much as one third of their actual translation time on various external consultations of resources. From a methodological point of view, investigating the use of resources in the translation process has proven to be a difficult task, even in the relatively uniform working environments of the pre-Internet era. Now that most external resources have moved from paper to online and have started to merge into the complex, heavily technologised translation environments, these investigations have become even more demanding. The present chapter explores the various challenges of conducting research into the use of external resources over the last few decades and presents a multi-method approach suited to the complex translation environment of today. It draws on findings from a recent study into the use of resources by professional translators showing how the adoption of this approach enabled a multi-dimensional exploration of translators’ research behaviours in their natural working environment and facilitated the subsequent classification of these behaviours into a Typology of Translator Research Styles (ttrs).

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Arda Tezcan, Véronique Hoste and Lieve Macken

Quality estimation (qe) and error analysis of machine translation (mt) output remain active areas in Natural Language Processing (nlp) research. Many recent efforts have focused on machine learning (ml) systems to estimate the mt quality, translation errors, post-editing speed or post-editing effort. As the accuracy of such ml tasks relies on the availability of corpora, there is an increasing need for large corpora of machine translations annotated with translation errors and the error annotation guidelines to produce consistent annotations. Drawing on previous work on translation error taxonomies, we present the scate (Smart Computer-aided Translation Environment) mt error taxonomy, which is hierarchical in nature and is based upon the familiar notions of accuracy and fluency. In the scate annotation framework, we annotate fluency errors in the target text and accuracy errors in both the source and target text, while linking the source and target annotations. We also propose a novel method for alignment-based inter-annotator agreement (iaa) analysis and show that this method can be used effectively on large annotation sets. Using the scate taxonomy and guidelines, we create the first corpus of mt errors for the English-Dutch language pair, consisting of statistical machine translation (smt) and rule-based machine translation (rbmt) errors, which is a valuable resource not only for nlp tasks in this field but also to study the relationship between mt errors and post-editing efforts in the future. Finally, we analyse the error profiles of the smt and the rbmt systems used in this study and compare the quality of these two different mt architectures based on the error types.

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Anna Zaretskaya, Gloria Corpas Pastor and Míriam Seghiri

Electronic tools have become an important part of a translator’s work. However, professional translators are not always satisfied with the tools they have at their disposal. In addition, many translators are not aware of all the existing types of tools they can use. In this way, it is necessary to investigate translators’ needs regarding electronic tools, as well as to provide them with the necessary training to help adopt them. In this article we discuss different methods that can be applied to investigate user requirements in the context of translation tools. User surveys are one of the most popular methods. We present the process of implementation and the results of a user survey on translation technologies focusing on different factors that influence translators’ adoption of tools, such as their education and computer competence. We also discuss translators’ preferences regarding features and characteristics of computer-assisted translation (cat) tools. The findings of the survey show that translators do not only expect their cat tools to have a full set of features, but also to be easy to use and intuitive. We suggest that usability of translation tools is closely related to the users’ productivity, which has to be taken into account when investigating translators’ needs regarding electronic tools.

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Hernani Costa, Gloria Corpas Pastor and Isabel Durán-Muñoz

This chapter describes and compares current Terminology Management Systems (tms) with a view to establishing a set of features in order to assess the extent to which terminology tools meet the specific needs of interpreters. As in translation, domain-specific terminology becomes a cornerstone in interpreting when consistency and accuracy are at stake. Therefore, an efficient use and management of terminology will enhance interpreting results. As a matter of fact, interpreters have limited time to prepare for new topics and they have to carry out searches and preparation prior to an interpretation and have it accessible during the interpreting service. Fortunately, there are an ever-growing number of applications capable of assisting interpreters before and during an interpretation service, even though they are still few compared to those devoted to translators. Although these tools appear to be quite similar, they provide different kinds of features, which result in different degrees of usefulness, as it can be observed in the last section of this paper.

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Éric Poirier

This chapter describes how human translation (ht) technology and natural language processing (nlp) applications can be of use in the design of meaning-based translation learning activities for a professional translation training course. Meaning-based translation learning activities are part of a new instrumental approach aiming at the operationalisation of meaning-based operations (source language understanding, meaning transfer, target language drafting) through iterative and replicable learning tasks. The instrumental approach makes use of ht technology as one of the three groups of translation tools identified by Bowker (2002) which also includes computer-aided translation (cat), the commonly-used term for machine-assisted translation (mat), and machine translation (mt), a diminutive of human-assisted machine translation (hamt). The instrumental approach involves task-based and objectively assessable and replicable learning activities on processing meaning in translation operations. The activities suggested in this chapter are all replicable in different language pairs and involve the processing of meaning by means of ht and nlp applications. They are also measurable in the context of grade-based assessment and traditional (instructional) teaching practices. To the best of our knowledge, those activities with their intensive use of ht and nlp applications have not been suggested elsewhere. The instrumental approach is centered on what technology and tools can do in the resolution of meaning-based translation difficulties and in the validation of correct performing of crucial translation operations.

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Aurélie Picton, Emmanuel Planas and Amélie Josselin-Leray

Abstract

In this chapter, we discuss the impact of the integration of digital linguistic data into the software environment of translators. We have studied more particularly the contribution of knowledge-rich contexts (KRCs, Meyer, 2001) to specialised translation. We carried out our research within the framework of the anr cristal which is funded by the French government (ANR-12-CORD-0020), and whose main objective is to develop innovative techniques for extracting krcs useful for translators from comparable corpora. Our strategy consisted in testing the use of different types of krc by translators working in a cat environment specifically designed for our experiment. Our careful observation of translators’ behaviour and the significant number of participants (42) have led us to draw some initial conclusions about the characteristics and patterns of the use of krcs, and their complementarity with traditional resources used by professional translators. This study provides us with a basis for further related research both on the ergonomics of computer-assisted translation tools and the integration of new resources useful for translators.

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John Moran, David Lewis and Christian Saam

The analysis of User Activity Data in software applications is now a common technique. For example, data is mined from large volumes of logs that record how users interact with web-application sites like amazon.com. Taking a similar approach, our research question is whether the analysis of this data from a Computer-aided Translation (cat) tool used in large running translation projects can help us better understand how translators interact with machine translation (mt). In the short term, these productivity analyses help buyers and translators base per-word pricing conversations for projects that use Machine Translation on hard data. In the long term, we believe the analysis of User Activity Data may help optimise translation technology development and translator training using various computational linguistic aids like predictive typing, interactive mt, full-sentence mt and automatic speech recognition. To solve this problem, we have developed an instrumented version of a well-known free open-source desktop-based cat tool called OmegaT we called iOmegaT. In this chapter, we describe iOmegaT in more detail, including design decisions we made. We also discuss some data we have analysed, how the system is used in commercial translation projects and how we think the data could be gathered from a wider range of cat tools while accounting for data privacy concerns.

Series:

Claudio Fantinuoli

Abstract

During the last decades, information technology has played a central role in the language services industry. Translators and technical writers take advantage of dedicated software to reuse already translated texts, to adhere to a customer-specific corporate language, to grant terminology consistency, and so forth. The final goal is to increase quality and productivity. Even if information technology did not have the same impact on conference interpreting, also the profession is undergoing some changes. Computer-assisted interpreting (cai) tools have entered the profession only in recent years, but other, more general resources had already influenced the way interpreters work. This is not only challenging the way interpreting is performed, but it may have an impact on the cognitive processes underlying the interpreting task, even on some basic assumptions and theories of interpreting, for example the cognitive load distribution between different tasks during simultaneous interpreting. Yet, the academic debate is starting to take notice of these changes and their implications only now. As a consequence, it almost failed to shed light on and address the challenges that lay ahead: there have been relatively few empirical investigations on the impact of cai tools; interpreting models have not been adapted accordingly; the didactics of interpreting has received almost no new technologies in their curricula and no proposal has been advanced to increase the quality of cai tools and to meet interpreters’ real needs.