Abstract This thesis handles special translation software, the mastery of which is becoming one of the basic requirements of successful translation work. The theoretical part describes the historical development, classification and main functions of translation memory systems. The thesis will further attempt to determine the criteria for the effective use of CAT tools and explore the text types and sorts for which the translation memory systems are most commonly used in the translation process. The functional view of the language-based text typology and the principles on which the translation memory systems work will also be handled. The practical part compares the result of a translation process (translation as a product) with and without CAT tools. The corpus of parallel texts (original translation) will be subjected to a translation analysis. This analysis concludes the levels which are affected by differences between translations made with and without CAT tools. The differences in the actual translation process with and without CAT tools which are not empirically verifiable will be analysed based on a survey conducted amongst translators. Then, the empirical part of the findings are summarized and systemized. The last chapter deals with the expected development in the translation market, the...

Text in English / Abstracts in English and Venda / This study has investigated the effectiveness of available tools used for translating from English into Tshivenḓa and vice versa with the aim to investigate and determine the effectiveness of these tools. This study dealt with the problem of lack of effective translation tools used to translate between English and Tshivenḓa. Tshivenḓa is one of South Africa’s minority languages. Its (Tshivenḓa) lack of effective translation tools negatively affects language practitioners’ work. This situation is perilous for translation quality assurance. Translation tools, both computer technology and non-computer technology tools abound for developed languages such as English, French and others. Based on the results of this research project, the researcher did make recommendations that could remedy the situation. South Africa is a democratic country that has a number of language-related policies. This then creates a conducive context for stakeholders with language passion to fully develop Tshivenḓa language in all dimensions. The fact is that all languages have evolved and they were all underdeveloped. This vividly shows that Tshivenḓa language development is also possible just like Afrikaans, which never existed on earth before 1652. It (Afrikaans) has evolved and overtaken all indigenous South African languages. This study did review the literature regarding translation and translation tools. The literature was obtained from both published and unpublished sources. The study has used mixed methods research, i.e. quantitative and qualitative research methods. These methods successfully complemented each other throughout the entire research. Data were gathered through questionnaires and interviews wherein both open and closed-ended questions were employed. Both purposive/judgemental and snowball (chain) sampling have been applied in this study. Data analysis was addressed through a combination of methods owing to the nature of mixed methods research. Guided by analytic comparison approach when grouping together related data during data analysis and presentation, both statistical and textual analyses have been vital in this study. Themes were constructed to lucidly present the gathered data. At the last chapters, the researcher discussed the findings and evaluated the entire research before making recommendations and conclusion. / Iyi ṱhoḓisiso yo ita tsedzuluso nga ha kushumele kwa zwishumiswa zwi re hone zwine zwa shumiswa u pindulela u bva kha luambo lwa English u ya kha Tshivenḓa na u bva kha Tshivenḓa u ya kha English ndivho I ya u sedzulusa na u lavhelesa kushumele kwa izwi zwishumiswa uri zwi a thusa naa. Ino ṱhoḓisiso yo shumana na thaidzo ya ṱhahelelo ya zwishumiswa zwa u pindulela zwine zwa shumiswa musi hu tshi pindulelwa vhukati ha English na Tshivenḓa. Tshivenḓa ndi luṅwe lwa nyambo dza Afrika Tshipembe dzine dza ambiwa nga vhathu vha si vhanzhi. U shaea ha zwishumiswa zwa u pindulela zwine zwa shuma nga nḓila I thusaho zwi kwama mushumo wa vhashumi vha zwa nyambo nga nḓila I si yavhuḓi. Iyi nyimele I na mulingo u kwamaho khwaḽithi ya zwo pindulelwaho. Zwishumiswa zwa u pindulela, zwa thekhnoḽodzhi ya khomphiyutha na zwi sa shumisi thekhnoḽodzhi ya khomphiyutha zwo ḓalesa kha nyambo dzo bvelelaho u tou fana na kha English, French na dziṅwe. Zwo sendeka kha mvelelo dza ino thandela ya ṱhoḓisiso, muṱoḓisisi o ita themendelo dzine dza nga fhelisa thaidzo ya nyimele. Afrika Tshipembe ndi shango ḽa demokirasi ḽine ḽa vha na mbekanyamaitele dzo vhalaho nga ha dzinyambo. Izwi zwi ita uri hu vhe na nyimele ine vhafaramikovhe vhane vha funesa nyambo vha kone u bveledza Tshivenḓa kha masia oṱhe. Zwavhukuma ndi zwa uri nyambo dzoṱhe dzi na mathomo nahone dzoṱhe dzo vha dzi songo bvelela. Izwi zwi ita uri zwi vhe khagala uri luambo lwa Tshivenḓa na lwone lu nga bveledzwa u tou fana na luambo lwa Afrikaans lwe lwa vha lu si ho ḽifhasini phanḓa ha ṅwaha wa 1652. Ulu luambo (Afrikaans) lwo vha hone shangoni lwa mbo bveledzwa lwa fhira nyambo dzoṱhe dza fhano hayani Afrika Tshipembe. Kha ino ṱhoḓisiso ho vhaliwa maṅwalwa ane a amba nga ha u pindulela na nga ha zwishumiswa zwa u pindulela. Maṅwalwa e a vhalwa o wanala kha zwiko zwo kanḓiswaho na zwiko zwi songo kanḓiswaho. Ino ṱhoḓisiso yo shumisa ngona dza ṱhoḓisiso dzo ṱanganyiswaho, idzo ngona ndi khwanthithethivi na khwaḽithethivi. Idzi ngona dzo shumisana zwavhuḓisa kha ṱhoḓisiso yoṱhe. Data yo kuvhanganywa hu tshi khou shumiswa dzimbudziso na u tou vhudzisa hune afho ho shumiswa mbudziso dzo vuleaho na dzo valeaho. Ngona dza u nanga sambula muṱoḓisisi o shumisa khaṱulo yawe uri ndi nnyi ane a nga vha a na data yo teaho na u humbela vhavhudziswa uri vha bule vhaṅwe vhathu vha re na data yo teaho ino ṱhoḓisiso. viii Tsenguluso ya data ho ṱanganyiswa ngona dza u sengulusa zwo itiswa ngauri ṱhoḓisiso ino yo ṱanganyisa ngona dza u ita ṱhoḓisiso. Sumbanḓila ho shumiswa tsenguluso ya mbambedzo kha u sengulusa data. Data ine ya fana yo vhewa fhethu huthihi musi hu tshi khou senguluswa na u vhiga. Tsenguluso I shumisaho mbalo/tshivhalo (khwanthithethivi) na I shumisaho maipfi kha ino ngudo dzo shumiswa. Ho vhumbiwa dziṱhoho u itela u ṱana data ye ya kuvhanganywa. Ngei kha ndima dza u fhedza, muṱodisisi o rera nga ha mawanwa, o ṱhaṱhuvha ṱhoḓisiso yoṱhe phanḓa ha u ita themendelo na u vhina. / African Languages / M.A. (African Languages)

Rekenaars kan ’n nuttige rol speel in vertaling. Twee benaderings is vertaalgeheuestelsels en masjienvertaalstelsels. By hierdie twee tegnologieë word ’n vertaalgeheue gebruik—’n tweetalige versameling vorige vertalings. Hierdie proefskrif bied metodes aan om die kwaliteit van ’n vertaalgeheue te verbeter. ’n Masjienleerbenadering word gevolg om foutiewe inskrywings in ’n vertaalgeheue te identifiseer. ’n Verskeidenheid leerkenmerke in drie kategorieë word aangebied: kenmerke wat verband hou met tekslengte, kenmerke wat deur kwaliteittoetsers soos vertaaltoetsers, ’n speltoetser en ’n grammatikatoetser bereken word, asook statistiese kenmerke wat met behulp van eksterne data bereken word. Die evaluasie van vertaalgeheuestelsels is nog nie gestandaardiseer nie. In hierdie proefskrif word ’n verskeidenheid probleme met bestaande evaluasiemetodes uitgewys, en ’n verbeterde evaluasiemetode word ontwikkel. Deur die foutiewe inskrywings uit ’n vertaalgeheue te verwyder, is ’n kleiner, skoner vertaalgeheue beskikbaar vir toepassings. Eksperimente dui aan dat so ’n vertaalgeheue beter prestasie behaal in ’n vertaalgeheuestelsel. As ondersteunende bewys vir die waarde van ’n skoner vertaalgeheue word ’n verbetering ook aangedui by die opleiding van ’n masjienvertaalstelsel. / Computers can play a useful role in translation. Two approaches are translation memory systems and machine translation systems. With these two technologies a translation memory is used— a bilingual collection of previous translations. This thesis presents methods to improve the quality of a translation memory. A machine learning approach is followed to identify incorrect entries in a translation memory. A variety of learning features in three categories are presented: features associated with text length, features calculated by quality checkers such as translation checkers, a spell checker and a grammar checker, as well as statistical features computed with the help of external data. The evaluation of translation memory systems is not yet standardised. This thesis points out a number of problems with existing evaluation methods, and an improved evaluation method is developed. By removing the incorrect entries in a translation memory, a smaller, cleaner translation memory is available to applications. Experiments demonstrate that such a translation memory results in better performance in a translation memory system. As supporting evidence for the value of a cleaner translation memory, an improvement is also achieved in training a machine translation system. / School of Computing / Ph. D. (Rekenaarwetenskap)

La mémoire de traduction (MT) joue un rôle décisif lors de la traduction et constitue une base de données idéale pour la plupart des professionnels de la langue. Cependant, une MT est très sujète au bruit et, en outre, il n’y a pas de source spécifique. Des efforts importants ont été déployés pour nettoyer des MT, en particulier pour former un meilleur système de traduction automatique. Dans cette thèse, nous essayons également de nettoyer la MT mais avec un objectif plus large : maintenir sa qualité globale et la rendre suffisament robuste pour un usage interne dans les institutions. Nous proposons un processus en deux étapes : d’abord nettoyer une MT institutionnelle (presque propre), c’est-à-dire éliminer le bruit, puis détecter les textes traduits à partir de systèmes neuronaux de traduction. Pour la tâche d’élimination du bruit, nous proposons une architecture impliquant cinq approches basées sur l’heuristique, l’ingénierie fonctionnelle et l’apprentissage profond. Nous évaluons cette tâche à la fois par annotation manuelle et traduction automatique (TA). Nous signalons un gain notable de +1,08 score BLEU par rapport à un système de nettoyage état de l’art. Nous proposons également un outil Web qui annote automatiquement les traductions incorrectes, y compris mal alignées, pour les institutions afin de maintenir une MT sans erreur. Les modèles neuronaux profonds ont considérablement amélioré les systèmes MT, et ces systèmes traduisent une immense quantité de texte chaque jour. Le matériel traduit par de tels systèmes finissent par peuplet les MT, et le stockage de ces unités de traduction dans TM n’est pas idéal. Nous proposons un module de détection sous deux conditions: une tâche bilingue et une monolingue (pour ce dernier cas, le classificateur ne regarde que la traduction, pas la phrase originale). Nous rapportons une précision moyenne d’environ 85 % en domaine et 75 % hors domaine dans le cas bilingue et 81 % en domaine et 63 % hors domaine pour le cas monolingue en utilisant des classificateurs d’apprentissage profond. / Translation Memory (TM) plays a decisive role during translation and is the go-to database for most language professionals. However, they are highly prone to noise, and additionally, there is no one specific source. There have been many significant efforts in cleaning the TM, especially for training a better Machine Translation system. In this thesis, we also try to clean the TM but with a broader goal of maintaining its overall quality and making it robust for internal use in institutions. We propose a two-step process, first clean an almost clean TM, i.e. noise removal and then detect texts translated from neural machine translation systems. For the noise removal task, we propose an architecture involving five approaches based on heuristics, feature engineering, and deep-learning and evaluate this task by both manual annotation and Machine Translation (MT). We report a notable gain of +1.08 BLEU score over a state-of-the-art, off-the-shelf TM cleaning system. We also propose a web-based tool “OSTI: An Open-Source Translation-memory Instrument” that automatically annotates the incorrect translations (including misaligned) for the institutions to maintain an error-free TM. Deep neural models tremendously improved MT systems, and these systems are translating an immense amount of text every day. The automatically translated text finds a way to TM, and storing these translation units in TM is not ideal. We propose a detection module under two settings: a monolingual task, in which the classifier only looks at the translation; and a bilingual task, in which the source text is also taken into consideration. We report a mean accuracy of around 85% in-domain and 75% out-of-domain for bilingual and 81% in-domain and 63% out-of-domain from monolingual tasks using deep-learning classifiers.