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Gloria Corpas Pastor and Isabel Durán-Muñoz

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Gloria Corpas Pastor and Isabel Durán-Muñoz

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Gloria Corpas Pastor and Isabel Durán-Muñoz

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Celia Rico, Pilar Sánchez-Gijón and Olga Torres-Hostench

The emergence of machine translation (mt) in professional translation practice has evolved from a topic of conversation among practitioners to promote a tangible change in the translation industry. The aim of this chapter is, then, to shed light on mt in professional and academic contexts by promoting a fresh approach to teaching using translation technology, and dealing with the needs and expectations of translators. Our work stems from considering the following key question: if the translation industry already considers post-editing as a viable service for almost any translation area, how should the academic world respond to this challenge? This question is addressed from three perspectives: (a) the evolution of translation technology and how post-editing has had an impact on the industry; (b) academic research paths in post-editing; and (c) training post-editors in a higher education context.

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Pierrette Bouillon, Johanna Gerlach, Asheesh Gulati, Victoria Porro and Violeta Seretan

The advance of machine translation in the last years is placing new demands on professional translators. This entails new requirements on translation educational curricula at the university level and exacerbates the need for dedicated software for teaching students how to leverage the technologies involved in a machine translation workflow. In this chapter, we introduce the ACCEPT Academic Portal, a user centred online platform which implements the complete machine translation (mt) workflow and is specifically designed for teaching purposes. Its ultimate objective is to increase the understanding of pre-editing, post-editing and evaluation of machine translation. The platform, publicly available at http://accept-portal.unige.ch/academic, is built around four main modules, namely, the Pre-editing, Machine Translation, Post-editing, and Evaluation module. The Pre-editing module provides checking resources to verify the compliance of the input text with automatic and interactive pre-editing rules, based on a shallow analysis of the text. The Translation module translates the raw and pre-edited versions of the input text using a statistical mt system, and highlights the differences between the two translations for easy identification of the impact of pre-editing on translation. The Post editing module allows users to improve translations by post-editing the output text freely, manually or with the help of interactive and automatic post-editing rules. Finally, the Evaluation module provides support for eliciting user feedback. At the end of the workflow, a summary and statistics on the whole process are made available to users, for reference purposes. The ACCEPT Academic Portal was developed in the framework of the ACCEPT European project and, to the best of our knowledge, it is the only online environment integrating advanced pre-editing and post-editing technology into a complete mt workflow. Through its simple and user-friendly interface, as well as its pedagogically motivated functionalities that enable experimentation, visual comparison and documentation, the ACCEPT Academic Portal is a unique tool allowing to study the interactions between mt-related processes and to assess the contribution of new technologies to translation.

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Claudio Fantinuoli

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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.

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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.

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Aurélie Picton, Emmanuel Planas and Amélie Josselin-Leray

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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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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.