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Informatics
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Advances in Computer-Aided Translation Technology
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Advances in Computer-Aided Translation Technology

A special issue of Informatics (ISSN 2227-9709).

Deadline for manuscript submissions: closed (30 April 2019) | Viewed by 50387

Special Issue Editors

Prof. Dr. Lieve Macken

E-Mail Website
Guest Editor
Department of Translation, Interpreting and Communication, Ghent University, 9000 Gent, Belgium
Interests: computer-assisted translation; machine translation; post-editing; parallel corpora; terminology extraction
Dr. Joke Daems

E-Mail Website
Guest Editor
Department of Translation, Interpreting and Communication, Ghent University, 9000 Gent, Belgium
Interests: computer-assisted translation; machine translation; post-editing; translatability; translation quality assessment; human–computer interaction; collaborative translation
Dr. Arda Tezcan

E-Mail Website
Guest Editor
Department of Translation, Interpreting and Communication, Ghent University, 9000 Gent, Belgium
Interests: computer-assisted translation; machine translation; automatic evaluation and quality estimation of machine translation output; machine learning; post-editing

Special Issue Information

Dear Colleagues,

Translation technology has become an integral part of the life of a professional translator. Computer-aided translation (CAT) tools have evolved over the years from basic translation memory systems to full-fledged translation environment tools (TEnTs), offering a wide range of support to the professional translator. Moreover, these environments attempt to reach the optimal level of human–machine interactions by increasingly integrating translation memory (TM) and machine translation (MT) suggestions in more interactive ways. However, with the growing variety of MT paradigms and changing translation work flows (e.g. collaborative translation), new challenges lie ahead.

For this Special Issue we seek novel, original contributions across the entire spectrum of computer-aided translation technology, covering advances in the

  • Matching and retrieval of segments in translation memories
  • Integration of TM and MT suggestions
  • Integration of client-specific terminology in neural MT
  • Multilingual terminology extraction
  • Quality estimation of MT and TM suggestions
  • Translation quality assurance
  • Automatic methods for translation memory cleaning and maintenance
  • Productivity measurements
  • Effort prediction and price estimation
  • Methods for collaborative translation
  • Post-editing guidelines and best practices
  • Intelligent interface design
  • User-adaptive systems
  • Automatic speech recognition for dictating translations
  • Integration with text authoring tools

Prof. Dr. Lieve Macken
Dr. Joke Daems
Dr. Arda Tezcan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Informatics is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (6 papers)

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Research

20 pages, 2013 KiB  
Article
Speech Synthesis in the Translation Revision Process: Evidence from Error Analysis, Questionnaire, and Eye-Tracking
by Dragoş Ciobanu, Valentina Ragni and Alina Secară
Informatics 2019, 6(4), 51; https://doi.org/10.3390/informatics6040051 - 11 Nov 2019
Cited by 7 | Viewed by 7115
Abstract
Translation revision is a relevant topic for translator training and research. Recent technological developments justify increased focus on embedding speech technologies—speech synthesis (text-to-speech) and speech recognition (speech-to-text)—into revision workflows. Despite some integration of speech recognition into computer-assisted translation (CAT)/translation environment tools (TEnT)/Revision tools, [...] Read more.
Translation revision is a relevant topic for translator training and research. Recent technological developments justify increased focus on embedding speech technologies—speech synthesis (text-to-speech) and speech recognition (speech-to-text)—into revision workflows. Despite some integration of speech recognition into computer-assisted translation (CAT)/translation environment tools (TEnT)/Revision tools, to date we are unaware of any CAT/TEnT/Revision tool that includes speech synthesis. This paper addresses this issue by presenting initial results of a case study with 11 participants exploring if and how the presence of sound, specifically in the source text (ST), affects revisers’ revision quality, preference and viewing behaviour. Our findings suggest an improvement in revision quality, especially regarding Accuracy errors, when sound was present. The majority of participants preferred listening to the ST while revising, but their self-reported gains on concentration and productivity were not conclusive. For viewing behaviour, a subset of eye-tracking data shows that participants focused more on the target text (TT) than the source regardless of the revising condition, though with differences in fixation counts, dwell time and mean fixation duration (MDF). Orientation and finalisation phases were also identified. Finally, speech synthesis appears to increase perceived alertness, and may prompt revisers to consult external resources more frequently. Full article
(This article belongs to the Special Issue Advances in Computer-Aided Translation Technology)
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29 pages, 6274 KiB  
Article
Translation Quality and Error Recognition in Professional Neural Machine Translation Post-Editing
by Jennifer Vardaro, Moritz Schaeffer and Silvia Hansen-Schirra
Informatics 2019, 6(3), 41; https://doi.org/10.3390/informatics6030041 - 17 Sep 2019
Cited by 22 | Viewed by 10043
Abstract
This study aims to analyse how translation experts from the German department of the European Commission’s Directorate-General for Translation (DGT) identify and correct different error categories in neural machine translated texts (NMT) and their post-edited versions (NMTPE). The term translation expert encompasses translator [...] Read more.
This study aims to analyse how translation experts from the German department of the European Commission’s Directorate-General for Translation (DGT) identify and correct different error categories in neural machine translated texts (NMT) and their post-edited versions (NMTPE). The term translation expert encompasses translator, post-editor as well as revisor. Even though we focus on neural machine-translated segments, translator and post-editor are used synonymously because of the combined workflow using CAT-Tools as well as machine translation. Only the distinction between post-editor, which refers to a DGT translation expert correcting the neural machine translation output, and revisor, which refers to a DGT translation expert correcting the post-edited version of the neural machine translation output, is important and made clear whenever relevant. Using an automatic error annotation tool and the more fine-grained manual error annotation framework to identify characteristic error categories in the DGT texts, a corpus analysis revealed that quality assurance measures by post-editors and revisors of the DGT are most often necessary for lexical errors. More specifically, the corpus analysis showed that, if post-editors correct mistranslations, terminology or stylistic errors in an NMT sentence, revisors are likely to correct the same error type in the same post-edited sentence, suggesting that the DGT experts were being primed by the NMT output. Subsequently, we designed a controlled eye-tracking and key-logging experiment to compare participants’ eye movements for test sentences containing the three identified error categories (mistranslations, terminology or stylistic errors) and for control sentences without errors. We examined the three error types’ effect on early (first fixation durations, first pass durations) and late eye movement measures (e.g., total reading time and regression path durations). Linear mixed-effects regression models predict what kind of behaviour of the DGT experts is associated with the correction of different error types during the post-editing process. Full article
(This article belongs to the Special Issue Advances in Computer-Aided Translation Technology)
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21 pages, 4662 KiB  
Article
Misalignment Detection for Web-Scraped Corpora: A Supervised Regression Approach
by Arne Defauw, Sara Szoc, Anna Bardadym, Joris Brabers, Frederic Everaert, Roko Mijic, Kim Scholte, Tom Vanallemeersch, Koen Van Winckel and Joachim Van den Bogaert
Informatics 2019, 6(3), 35; https://doi.org/10.3390/informatics6030035 - 01 Sep 2019
Cited by 2 | Viewed by 5383
Abstract
To build state-of-the-art Neural Machine Translation (NMT) systems, high-quality parallel sentences are needed. Typically, large amounts of data are scraped from multilingual web sites and aligned into datasets for training. Many tools exist for automatic alignment of such datasets. However, the quality of [...] Read more.
To build state-of-the-art Neural Machine Translation (NMT) systems, high-quality parallel sentences are needed. Typically, large amounts of data are scraped from multilingual web sites and aligned into datasets for training. Many tools exist for automatic alignment of such datasets. However, the quality of the resulting aligned corpus can be disappointing. In this paper, we present a tool for automatic misalignment detection (MAD). We treated the task of determining whether a pair of aligned sentences constitutes a genuine translation as a supervised regression problem. We trained our algorithm on a manually labeled dataset in the FR–NL language pair. Our algorithm used shallow features and features obtained after an initial translation step. We showed that both the Levenshtein distance between the target and the translated source, as well as the cosine distance between sentence embeddings of the source and the target were the two most important features for the task of misalignment detection. Using gold standards for alignment, we demonstrated that our model can increase the quality of alignments in a corpus substantially, reaching a precision close to 100%. Finally, we used our tool to investigate the effect of misalignments on NMT performance. Full article
(This article belongs to the Special Issue Advances in Computer-Aided Translation Technology)
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15 pages, 289 KiB  
Article
Post-Editing Neural MT in Medical LSP: Lexico-Grammatical Patterns and Distortion in the Communication of Specialized Knowledge
by Hanna Martikainen
Informatics 2019, 6(3), 26; https://doi.org/10.3390/informatics6030026 - 30 Jun 2019
Cited by 6 | Viewed by 7305
Abstract
The recent arrival on the market of high-performing neural MT engines will likely lead to a profound transformation of the translation profession. The purpose of this study is to explore how this paradigm change impacts the post-editing process, with a focus on lexico-grammatical [...] Read more.
The recent arrival on the market of high-performing neural MT engines will likely lead to a profound transformation of the translation profession. The purpose of this study is to explore how this paradigm change impacts the post-editing process, with a focus on lexico-grammatical patterns that are used in the communication of specialized knowledge. A corpus of 109 medical abstracts pre-translated from English into French by the neural MT engine DeepL and post-edited by master’s students in translation was used to study potential distortions in the translation of lexico-grammatical patterns. The results suggest that neural MT leads to specific sources of distortion in the translation of these patterns, not unlike what has previously been observed in human translation. These observations highlight the need to pay particular attention to lexico-grammatical patterns when post-editing neural MT in order to achieve functional equivalence in the translation of specialized texts. Full article
(This article belongs to the Special Issue Advances in Computer-Aided Translation Technology)
36 pages, 1944 KiB  
Article
Improving the Translation Environment for Professional Translators
by Vincent Vandeghinste, Tom Vanallemeersch, Liesbeth Augustinus, Bram Bulté, Frank Van Eynde, Joris Pelemans, Lyan Verwimp, Patrick Wambacq, Geert Heyman, Marie-Francine Moens, Iulianna van der Lek-Ciudin, Frieda Steurs, Ayla Rigouts Terryn, Els Lefever, Arda Tezcan, Lieve Macken, Véronique Hoste, Joke Daems, Joost Buysschaert, Sven Coppers, Jan Van den Bergh and Kris Luytenadd Show full author list remove Hide full author list
Informatics 2019, 6(2), 24; https://doi.org/10.3390/informatics6020024 - 20 Jun 2019
Cited by 6 | Viewed by 11232
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Advances in Computer-Aided Translation Technology)
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21 pages, 3315 KiB  
Article
Creating a Multimodal Translation Tool and Testing Machine Translation Integration Using Touch and Voice
by Carlos S. C. Teixeira, Joss Moorkens, Daniel Turner, Joris Vreeke and Andy Way
Informatics 2019, 6(1), 13; https://doi.org/10.3390/informatics6010013 - 25 Mar 2019
Cited by 11 | Viewed by 8112
Abstract
Commercial software tools for translation have, until now, been based on the traditional input modes of keyboard and mouse, latterly with a small amount of speech recognition input becoming popular. In order to test whether a greater variety of input modes might aid [...] Read more.
Commercial software tools for translation have, until now, been based on the traditional input modes of keyboard and mouse, latterly with a small amount of speech recognition input becoming popular. In order to test whether a greater variety of input modes might aid translation from scratch, translation using translation memories, or machine translation postediting, we developed a web-based translation editing interface that permits multimodal input via touch-enabled screens and speech recognition in addition to keyboard and mouse. The tool also conforms to web accessibility standards. This article describes the tool and its development process over several iterations. Between these iterations we carried out two usability studies, also reported here. Findings were promising, albeit somewhat inconclusive. Participants liked the tool and the speech recognition functionality. Reports of the touchscreen were mixed, and we consider that it may require further research to incorporate touch into a translation interface in a usable way. Full article
(This article belongs to the Special Issue Advances in Computer-Aided Translation Technology)
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