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In this work, we analyse translated texts in terms of various features. We compare two types of human translations, professional and students’, and machine translation (MT) outputs in terms of lexical and grammatical variety, sentence length, as well as frequencies of different part-of-speech (POS) tags and POS-trigrams. Our analyses are carried out on parallel translations into Croatian, Finnish and Russian, all originating from the same source English texts. Our results indicate that machine translations are the closest to the source text, followed by student translations. Also, student translations are sometimes more similar to MT than to professional translations. Furthermore, we identify sets of features distinctive for machine translations.

Linguistic accessibility presents a challenge for public broadcasters. While demand for multilingual content to support accessibility is growing, limited resources do not allow translating to a wide range of languages. One possibility to increase linguistic accessibility in media would be to provide automated translations into languages that cannot be served otherwise. However, implementing automation with the objective of supporting linguistic accessibility requires careful, proactive investigation together with the prospective audience. This article explores automated interlingual subtitling from the audience's perspective, based on focus group discussions and an online survey conducted in association with the Finnish public broadcaster Yle. We investigated English-speaking viewers’ reactions to automated English subtitles in Finnish-language news and current affairs video clips. Our analysis indicates that while viewers are able to understand the gist of a programme with automated subtitles, shortcomings in the quality of automated speech recognition, translation and timing of subtitles result in significant cognitive load that limits the usability of the subtitles. Participants expressed clear interest in automated subtitles for breaking news and other important local content, as well as to facilitate access to local culture and entertainment. However, quality improvements are required before automated subtitles can be deployed.

Digital tools are changing not only the process of translating and interpreting, but also the industry as a whole, societal perception and research in translation and interpreting. This Tradumàtica Special Issue collects research on some of these topics, highlighting the importance of furthering research on human-computer interaction in translation and interpreting studies. Les eines digitals no només estan canviant la manera de traduir i interpretar, sinó també la indústria en el seu conjunt, la percepció de la societat i la recerca en traducció i interpretació. Aquest número especial de Tradumàtica recull investigacions sobre alguns d'aquests temes, destacant la importància d'aprofundir en la investigació sobre la interacció persona-ordinador en els estudis de traducció i interpretació. Las herramientas digitales no solo están cambiándola manera de traducir e interpretar, sino también la industria en su conjunto, la percepción de la sociedad y la investigación en traducción e interpretación. Este número especial de Tradumàtica recoge investigaciones sobre algunos de estos temas, destacando la importancia de profundizar en la investigación sobre la interacción persona-ordenador en los estudios de traducción e interpretación.

Digitalisaatio ja teknologia ovat muokanneet kääntämistä monin tavoin. Käännösteknologia voi tukea käännöstyötä, yhteistoimintaa ja monikielisen tiedon saatavuutta. Käännösteknologiaa ja erityisesti konekääntämistä käsittelevät diskurssit kuitenkin heijastavat niin sanottua heteromaatiota: keskiöön käännöstyön tekijänä ja arvon tuottajana nostetaan teknologia ihmisen sijaan. Samanaikaisesti kääntäminen pilkotaan teknologiavälitteisissä prosesseissa kokonaisuudesta irrotettuihin paloihin ja kääntäjien toimintaa seurataan ja mitataan jatkuvasti digitaalisen taylorismin nimissä. Artikkeli analysoi teknologian vaikutuksia kääntämisen käsityksiin ja käännöstoimintaan digitaalisen taylorismin ja heteromaation käsitteiden valossa sekä tarkastelee teknologian mahdollista roolia sosioekonomisesti kestävämmän käännöstalouden rakentamisessa. * * * Digitalisation and technology have shaped translation in many ways. Technology can support translation activity, collaboration and multilingual information access. Discourses about translation technology, however, often reflect so-called heteromation: the machine is centralised as producer of value instead of the translator. Technology also enables dividing translation into de-contextualised pieces and constant monitoring of translators in the name of digital taylorism. This article examines the impact of new technologies on translation practices in the light of the concepts of digital taylorism and heteromation, and addresses the potential role of technology in building a more sustainable translation practices. Keywords: translation technology, machine translation, digital taylorism, heteromation, sustainability

The LITHME COST Action brings together researchers from various fields of study focusing on language and technology. We present the overall goals of LITHME and the network’s working groups focusing on diverse questions related to language and technology. As an example of the work of the LITHME network, we discuss the working group on language work and language professionals. nonPeerReviewed

Neural machine translation (NMT) systems are nowadays essential components of professional translation workflows. Consequently, human translators are increasingly working as post-editors for machine-translated content. The NWO-funded InDeep project aims to empower users of Deep Learning models of text, speech, and music by improving their ability to interact with such models and interpret their behaviors. In the specific context of translation, we aim at developing new tools and methodologies to improve prediction attribution, error analysis, and controllable generation for NMT systems. These advances will be evaluated through field studies involving professional translators to assess gains in efficiency and overall enjoyability of the post-editing process.

We present here the EU-funded project CREAMT that seeks to understand what is meant by creativity in different translation modalities, e.g. machine translation, post-editing or professional translation. Focusing on the textual elements that determine creativity in translated literary texts and the reader experience, CREAMT uses a novel, interdisciplinary approach to assess how effective MT is in literary translation considering creativity in translation and the ultimate user: the reader.

This paper describes a new corpus of human translations which contains both professional and students translations. The data consists of English sources — texts from news and reviews — and their translations into Russian and Croatian, as well as of the subcorpus containing translations of the review texts into Finnish. All target languages represent mid-resourced and less or mid-investigated ones. The corpus will be valuable for studying variation in translation as it allows a direct comparison between human translations of the same source texts. The corpus will also be a valuable resource for evaluating machine translation systems. We believe that this resource will facilitate understanding and improvement of the quality issues in both human and machine translation. In the paper, we describe how the data was collected, provide information on translator groups and summarise the differences between the human translations at hand based on our preliminary results with shallow features.

We address the task of automatically distinguishing between human-translated (HT) and machine translated (MT) texts. Following recent work, we fine-tune pre-trained language models (LMs) to perform this task. Our work differs in that we use state-of-the-art pre-trained LMs, as well as the test sets of the WMT news shared tasks as training data, to ensure the sentences were not seen during training of the MT system itself. Moreover, we analyse performance for a number of different experimental setups, such as adding translationese data, going beyond the sentence-level and normalizing punctuation. We show that (i) choosing a state-of-the-art LM can make quite a difference: our best baseline system (DeBERTa) outperforms both BERT and RoBERTa by over 3% accuracy, (ii) adding translationese data is only beneficial if there is not much data available, (iii) considerable improvements can be obtained by classifying at the document-level and (iv) normalizing punctuation and thus avoiding (some) shortcuts has no impact on model performance.

We introduce the project MaCoCu: Massive collection and curation of monolingual and bilingual data: focus on under-resourced languages, funded by the Connecting Europe Facility, which is aimed at building monolingual and parallel corpora for under-resourced European languages. The approach followed consists of crawling large amounts of textual data from selected top-level domains of the Internet, and then applying a curation and enrichment pipeline. In addition to corpora, the project will release the free/open-source web crawling and curation software used.

Les eines digitals no només estan canviant la manera de traduir i interpretar, sinó també la indústria en el seu conjunt, la percepció de la societat i la recerca en traducció i interpretació. Aquest número especial de Tradumàtica recull investigacions sobre alguns d'aquests temes, destacant la importància d'aprofundir en la investigació sobre la interacció persona-ordinador en els estudis de traducció i interpretació. Digital tools are changing not only the process of translating and interpreting, but also the industry as a whole, societal perception of and research in translation and interpreting. This Tradumàtica Special Issue collects research on some of these topics, highlighting the importance of furthering research on human-computer interaction in translation and interpreting studies. Las herramientas digitales no solo están cambiando la manera de traducir e interpretar, sino también la industria en su conjunto, la percepciónde la sociedad y la investigación en traducción e interpretación. Este número especial de Tradumàtica recoge investigaciones sobre algunos de estos temas, destacando la importancia de profundizar en la investigación sobre la interacción persona-ordenador en los estudios de traducción e interpretación.

In this deliverable, we report on our automatic content retrieval experiments and their implications for improving the discoverability of archive content, with a focus on cross-lingual retrieval, but also including our additional cross-modal retrieval tests. First, we introduce the methods we used to simulate a realistic mixed-language media archive using the raw data from a publicly available collection of annotated images. We discuss the ways in which automatic content retrieval on this archive parallels or diverges from content search in the MeMAD prototype platform (Limecraft Flow), to clarify the extent to which they overlap. Afterwards, we describe how we further processed the data, drawing from our expertise in machine translation and image processing in order to enrich the archive, and to improve content retrieval performance. Next, we provide our experimental findings from using textual metadata translations and automatically-generated image captions to expand the metadata, as well as our tests on performing retrieval beyond using simple textual search queries. Our findings unequivocally validate the utility of metadata translations for cross-lingual content retrieval, and further encourage additional venues for cross-modal and multimodal retrieval methods. We describe these findings in detail alongside the empirical scores we have obtained from our own evaluations, and conclude the report with our general impressions and the lessons we have learned from this study.

This paper presents a study of machine translation and post-editing in the field of audiovisual translation. We analyse user experience data collected from post-editing tasks completed by twelve translators in four language pairs. We also present feedback provided by the translators in semi-structured interviews. The results of the user experience survey and thematic analysis of interviews shows that the translators’ impression of post-editing subtitles was on average neutral to somewhat negative, with segmentation and timing of subtitles identified as a key factor. Finally, we discuss the implications of the issues arising from the user experience survey and interviews for the future development of automatic subtitle translation.

This paper presents a user evaluation of machine translation and post-editing for TV subtitles. Based on a process study where 12 professional subtitlers translated and post-edited subtitles, we compare effort in terms of task time and number of keystrokes. We also discuss examples of specific subtitling features like condensation, and how these features may have affected the post-editing results. In addition to overall MT quality, segmentation and timing of the subtitles are found to be important issues to be addressed in future work.

The aim of the present article is to show how the holistic approach of a “simulated translation bureau” (STB), bringing together many technological skills that would otherwise be taught separately in isolated course units, succeeds in familiarising translation students with the new ways of working and with new technologies.

This dissertation investigates the practice of machine translation post-editing and the various aspects of effort involved in post-editing work. Through analyses of edits made by post-editors, the work described here examines three main questions: 1) what types of machine translation errors or source text features cause particular effort in post-editing, 2) what types of errors can or cannot be corrected without the help of the source text, and 3) how different indicators of effort vary between different post-editors. The dissertation consists of six previously published articles, and an introductory summary. Five of the articles report original research, and involve analyses of post-editing data to examine questions related to post-editing effort as well as differences between post-editors. The sixth article is a survey presenting an overview of the research literature. The research reported is based on multiple datasets consisting of machine translations and their post-edited versions, as well as process and evaluation data related to post-editing effort. The dissertation presents a mixed methods study combining qualitative and quantitative approaches, as well as theoretical and analytical tools from the fields of language technology and translation studies. Data on edits performed by post-editors, post-editing time, keylogging data, and subjective evaluations of effort are combined with error analyses of the machine translations in question, and compared for various post-editors. The results of this dissertation provide evidence that, in addition to the number of edits performed, post-editing effort is affected by the type of edits as well as source text features. Secondly, the results show that while certain language errors can be corrected even without access to the source text, certain other types that more severely affect the meaning cannot. Thirdly, the results show that post-editors' speed and the amount of editing they perform differ, and that various profiles can be identified in terms of how the edits are planned and carried out by the post-editors. The results of this work may have both theoretical and practical implications for the measurement and estimation of post-editing effort. Väitöskirjani tutkimuskohteena on konekäännösten jälkieditointi ja editoinnin työläys. Konekäännöksen jälkieditoinnilla tarkoitetaan, että kääntimellä tuotetaan raakakäännös, jota kääntäjä muokkaa. Tällainen konekäännösten käyttö on viime vuosina yleistynyt, joskin Suomessa se on vielä vähäistä. Raakakäännöksen käytöstä on hyötyä käännösprosessissa vain, jos jälkieditointi on vähemmän työlästä kuin kääntäminen ilman raakakäännöstä. Siksi tarvitaan tietoa, miten konekäännöksen virheet vaikuttavat editointiin, milloin editointi on mielekästä ja milloin virheet tekevät raakakäännöksen käytön liian työlääksi. Tutkimuksessa tarkastelen editoijien tekemiä muutoksia ja analysoin, millaiset konekäännöksissä esiintyvät virheet tai lähtötekstien piirteet tekevät editoinnista erityisen työlästä. Tutkin, onnistuuko virheiden korjaaminen joissakin tilanteissa jopa ilman lähtötekstiä. Tarkastelen myös, miten työläys näkyy eri editoijien tekemien korjausten määrässä, editointinopeudessa ja editointitavoissa. Analyysi pohjautuu englanti-suomi- ja englanti-espanja-konekäännöksiin ja niiden jälkieditoituihin versioihin. Editoijien tekemien muutosten perusteella analysoin, millaisia virheitä he konekäännöksestä korjasivat ja millaisia virheitä ei pystytty korjaamaan, jos lähtötekstiä ei ollut käytettävissä. Virheanalyysiin yhdistin aineistoa editointiin kuluneesta ajasta ja tietokoneen näppäimistön käytöstä editoinnin aikana, sekä konekäännöstä arvioineiden henkilöiden subjektiivisia arvioita, kuinka työlääksi editointi koettiin. Analyysi yhdistää sekä laadullisia että määrällisiä menetelmiä. Tutkimuksen tulokset osoittavat, että konekäännökseen tehtyjen muutosten lukumäärän lisäksi myös muut tekijät, kuten virheiden tyyppi ja editoitavan virkkeen pituus, vaikuttavat sekä editointiaikaan että subjektiivisesti koettuun työläyteen. Tulokset myös viittaavat siihen, että tietyntyyppisten kielellisten virheiden korjaaminen onnistuu jopa ilman lähtötekstiä mutta vakavammin merkityksen välittymistä haittaavat virheet voivat tehdä korjaamisesta mahdotonta. Eri editoijien prosessien vertailu osoittaa myös, että editointinopeus ja korjausten määrä vaihtelee samaakin tekstiä editoitaessa, ja eri editoijat toteuttavat korjauksensa eri tavoin. Tutkimuksen tuloksia voidaan hyödyntää sekä konekääntimien että editointiprosessien kehittämisessä ja editoinnin työläyden arvioinnissa.

The use of automatic evaluation metrics to is well established in the translation industry. Whereas it is relatively easy to cover the word- and character-based metrics in an MT course, it is less obvious to integrate the newer neural metrics. In this paper we discuss how we introduced the topic of MT quality assessment in a course for translation students. We selected three English source texts, each having a different difficulty level and style, and let the students translate the texts into their L1 and reflect upon translation difficulty. Afterwards, the students were asked to assess MT quality for the same texts using different methods and to critically reflect upon obtained results. The students had access to the MATEO web interface, which contains wordand character-based metrics as well as neural metrics. The students used two different reference translations: their own translations and professional translations of the three texts. We not only synthesise the comments of the students, but also present the results of some cross-lingual analyses on nine different language pairs.

DUAL-T is a Marie Skłodowska-Curie Postdoctoral Fellowship project which aims at involving literary translators in the testing of technology-inclusive workflows. Participants will be asked to translate three short stories using, respectively, (1) a word processor combined with online resources, (2) a computer-aided translation (CAT) tool, and (3) a machine translation post-editing (MTPE) tool.

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This paper presents the project initiated by the BiasByUs team resulting from the 2021 Artificially Correct Hackaton. We briefly explain our winning participation in the hackaton, tackling the challenge on ‘Database and detection of gender bi-as in A.I. translations’, we highlight the importance of gender bias in Machine Translation (MT), and describe our pro-posed solution to the challenge, the cur-rent status of the project, and our envi-sioned future collaborations and re-search.

This paper presents the Multitask, Multilingual, Multimodal Language Generation COST Action - Multi3Generation (CA18231), an interdisciplinary network of research groups working on different aspects of language generation. This "meta-paper" will serve as reference for citations of the Action in future publications. It presents the objectives, challenges and a the links for the achieved outcomes.

This project aims to study the impact of adapting neural machine translation (NMT) systems through translation exemplars, determine the optimal similarity metric(s) for retrieving informative exemplars, and, verify the usefulness of this approach for domain adaptation of NMT systems.

The WiLMa project aims to assess the effects of using machine translation (MT) tools on the writing processes of second language (L2) learners of varying proficiency. Particular attention is given to individual variation in learners’ tool use.

This chapter presents a survey charting the revision policies of Finnish language service providers (LSPs) and proposes a visual model that can be used for developing the policies further in a systematic manner. The purpose of the survey is to learn more about real-life LSP practices in order to develop a theoretical model and a practical tool concerning the use of revision in translation production workflows. The survey results presented here focus on revision parameters, the allowed level of creativity, various cases that require a specific kind of revision, and decision-making power. It was found that there is much flexibility in practices, and the client’s wishes are generally considered paramount. To further develop translation workflows, it is proposed that revision tasks that differ from each other in purpose and in variables such as the text type, degree of creativity, practical procedure or revision parameters could be systematically defined using a revision continuum which would range from simple linguistic checking to creative editing and tailoring and would help secure the financial viability of LSPs’ operations by highlighting the appropriate pricing of different revision tasks. acceptedVersion

Translation Revision and Post-editing looks at the apparently dissolving boundary between correcting translations generated by human brains and those generated by machines. It presents new research on post-editing and revision in government and corporate translation departments, translation agencies, the literary publishing sector and the volunteer sector, as well as on training in both types of translation checking work. This collection includes empirical studies based on surveys, interviews and keystroke logging, as well as more theoretical contributions questioning such traditional distinctions as translating versus editing. The chapters discuss revision and post-editing involving eight languages: Afrikaans, Catalan, Dutch, English, Finnish, French, German and Spanish. Among the topics covered are translator/reviser relations and revising/post-editing by non-professionals. The book is key reading for researchers, instructors and advanced students in Translation Studies as well as for professional translators with a special interest in checking translations. (source: https://www.routledge.com/Translation-Revision-and-Post-editing-Industry-Practices-and-Cognitive/Koponen-Mossop-Robert-Scocchera/p/book/9781138549715)