The present study explored the use of translation technology in second language (L2) writing by English as a Second Language (ESL) students at the University level. The appropriate role of translation, and specifically translation technology, in L2 curricula has been the subject of theoretical and practical debate. In order to address knowledge gaps relevant to this debate, the present study sought to document students’ current use of translation technology, specifically free online translation (FOT) tools, and their opinions about these tools. The study’s mixed-methods design included video observations and questionnaires regarding FOT use completed by 19 university students enrolled in a high intermediate-level ESL course. Semi-structured follow-up interviews were conducted with the six participants who were observed using FOT tools extensively on the video recordings. Results showed that high intermediate-level ESL students have a primarily positive attitude toward FOT tools. In addition, the majority of students reported using such tools regularly, even though only about one third of the students were actually observed using the tools significantly in the video recordings. Results are discussed in the context of the ongoing debate over whether and how translation technology should be used in L2 classrooms.

Translation from one human language to another has been using the help of the capabilities of computer advances. There are a lot of machine translators nowadays, each adapts to different machine translator approaches. This thesis presents the distinction between two selected machine translator approaches, statistical machine translator (SMT) and hybrid machine translator (HMT). The research focuses on creating evaluation for two machine translator of different approaches by both textual studies and evaluation experiment. The result of this research is an evaluation of the translator system and also the translation result. This result is then hoped to add information into the history of machine translators. / Program: Kandidatutbildning i informatik

Most organizations use an increasing number of domain- or organization-specific words and phrases. A translation process, whether human or automated, must also be able to accurately and efficiently use these specific multilingual terminology collections. However, comparatively little has been done to explore the use of vetted terminology as an input to machine translation (MT) for improved results. In fact, no single established process currently exists to integrate terminology into MT as a general practice, and especially no established process for neural machine translation (NMT) exists to ensure that the translation of individual terms is consistent with an approved terminology collection. The use of tokenization as a method of injecting terminology and of evaluating terminology injection is the focus of this thesis. I use the attention mechanism prevalent in state-of-the-art NMT systems to produce the desired results. Attention vectors play an important part of this method to correctly identify semantic entities and to align the tokens that represent them. My methods presented in this thesis use these attention vectors to align the source tokens in the sentence to be translated with the target tokens in the final translation output. Then, supplied terminology is injected, where these alignments correctly identify semantic entities. My methods demonstrate significant improvement to the state-of-the-art results for NMT using terminology injection.

This thesis addresses the technical and linguistic aspects of discourse-level processing in phrase-based statistical machine translation (SMT). Connected texts can have complex text-level linguistic dependencies across sentences that must be preserved in translation. However, the models and algorithms of SMT are pervaded by locality assumptions. In a standard SMT setup, no model has more complex dependencies than an n-gram model. The popular stack decoding algorithm exploits this fact to implement efficient search with a dynamic programming technique. This is a serious technical obstacle to discourse-level modelling in SMT. From a technical viewpoint, the main contribution of our work is the development of a document-level decoder based on stochastic local search that translates a complete document as a single unit. The decoder starts with an initial translation of the document, created randomly or by running a stack decoder, and refines it with a sequence of elementary operations. After each step, the current translation is scored by a set of feature models with access to the full document context and its translation. We demonstrate the viability of this decoding approach for different document-level models. From a linguistic viewpoint, we focus on the problem of translating pronominal anaphora. After investigating the properties and challenges of the pronoun translation task both theoretically and by studying corpus data, a neural network model for cross-lingual pronoun prediction is presented. This network jointly performs anaphora resolution and pronoun prediction and is trained on bilingual corpus data only, with no need for manual coreference annotations. The network is then integrated as a feature model in the document-level SMT decoder and tested in an English–French SMT system. We show that the pronoun prediction network model more adequately represents discourse-level dependencies for less frequent pronouns than a simpler maximum entropy baseline with separate coreference resolution. By creating a framework for experimenting with discourse-level features in SMT, this work contributes to a long-term perspective that strives for more thorough modelling of complex linguistic phenomena in translation. Our results on pronoun translation shed new light on a challenging, but essential problem in machine translation that is as yet unsolved.

Die sukses van enige masjienvertaalsisteem hang grootliks van die hoeveelheid en kwaliteit van die beskikbare afrigtingsdata af. n Sisteem wat met foutiewe of lae–kwaliteit data afgerig is, sal uiteraard swakker afvoer lewer as n sisteem wat met korrekte of hoë–kwaliteit data afgerig is. In die geval van hulpbronarm tale waar daar min data beskikbaar is en data dalk noodgedwonge vertaal moet word vir die skep van parallelle korpora wat as afrigtingsdata kan dien, is dit dus baie belangrik dat die data wat vir vertaling gekies word, so gekies word dat dit teksgedeeltes insluit wat die meeste waarde tot die masjienvertaalsisteem sal bydra. Dit is ook in so n geval uiters belangrik om die beskikbare data so goed moontlik aan te wend. Hierdie studie stel ondersoek in na metodes om afrigtingsdata te selekteer met die doel om n optimale masjienvertaalsisteem met beperkte hulpbronne af te rig. Daar word ook aandag gegee aan die moontlikheid om die gewigte van sekere gedeeltes van die afrigtingsdata te verhoog om sodoende die data wat die meeste waarde tot die masjienvertaalsisteem bydra te beklemtoon. Alhoewel hierdie studie spesifiek gerig is op metodes vir dataselektering en –manipulering vir die taalpaar Engels–Afrikaans, sou die metodes ook vir toepassing op ander taalpare gebruik kon word. Die evaluasieproses dui aan dat beide die dataselekteringsmetodes, asook die aanpassing van datagewigte, n positiewe impak op die kwaliteit van die resulterende masjienvertaalsisteem het. Die uiteindelike sisteem, afgerig deur n kombinasie van verskillende metodes, toon n 2.0001 styging in die NIST–telling en n 0.2039 styging in die BLEU–telling. / Thesis (M.A. (Applied Language and Literary Studies))--North-West University, Potchefstroom Campus, 2011.

Die sukses van enige masjienvertaalsisteem hang grootliks van die hoeveelheid en kwaliteit van die beskikbare afrigtingsdata af. n Sisteem wat met foutiewe of lae–kwaliteit data afgerig is, sal uiteraard swakker afvoer lewer as n sisteem wat met korrekte of hoë–kwaliteit data afgerig is. In die geval van hulpbronarm tale waar daar min data beskikbaar is en data dalk noodgedwonge vertaal moet word vir die skep van parallelle korpora wat as afrigtingsdata kan dien, is dit dus baie belangrik dat die data wat vir vertaling gekies word, so gekies word dat dit teksgedeeltes insluit wat die meeste waarde tot die masjienvertaalsisteem sal bydra. Dit is ook in so n geval uiters belangrik om die beskikbare data so goed moontlik aan te wend. Hierdie studie stel ondersoek in na metodes om afrigtingsdata te selekteer met die doel om n optimale masjienvertaalsisteem met beperkte hulpbronne af te rig. Daar word ook aandag gegee aan die moontlikheid om die gewigte van sekere gedeeltes van die afrigtingsdata te verhoog om sodoende die data wat die meeste waarde tot die masjienvertaalsisteem bydra te beklemtoon. Alhoewel hierdie studie spesifiek gerig is op metodes vir dataselektering en –manipulering vir die taalpaar Engels–Afrikaans, sou die metodes ook vir toepassing op ander taalpare gebruik kon word. Die evaluasieproses dui aan dat beide die dataselekteringsmetodes, asook die aanpassing van datagewigte, n positiewe impak op die kwaliteit van die resulterende masjienvertaalsisteem het. Die uiteindelike sisteem, afgerig deur n kombinasie van verskillende metodes, toon n 2.0001 styging in die NIST–telling en n 0.2039 styging in die BLEU–telling. / Thesis (M.A. (Applied Language and Literary Studies))--North-West University, Potchefstroom Campus, 2011.

La traduction automatique (TA) a connu des progrès significatifs ces dernières années et continue de s'améliorer. La TA est utilisée aujourd'hui avec succès dans de nombreux contextes, y compris les environnements professionnels de traduction et les scénarios de production. Cependant, le processus de traduction requiert souvent des connaissances plus larges qu'extraites de corpus parallèles. Étant donné qu'une injection de connaissances humaines dans la TA est nécessaire, l'un des moyens possibles d'améliorer TA est d'assurer une collaboration optimisée entre l'humain et la machine. À cette fin, de nombreuses questions sont posées pour la recherche en TA: Comment détecter les passages où une aide humaine devrait être proposée ? Comment faire pour que les machines exploitent les connaissances humaines obtenues afin d'améliorer leurs sorties ? Enfin, comment optimiser l'échange: minimiser l'effort humain impliqué et maximiser la qualité de TA? Diverses solutions sont possibles selon les scénarios de traductions considérés. Dans cette thèse, nous avons choisi de nous concentrer sur la pré-édition, une intervention humaine en TA qui a lieu ex-ante, par opposition à la post-édition, où l'intervention humaine qui déroule ex-post. En particulier, nous étudions des scénarios de pré-édition ciblés où l'humain doit fournir des traductions pour des segments sources difficiles à traduire et choisis avec soin. Les scénarios de la pré-édition impliquant la pré-traduction restent étonnamment peu étudiés dans la communauté. Cependant, ces scénarios peuvent offrir une série d'avantages relativement, notamment, à des scénarios de post-édition non ciblés, tels que : la réduction de la charge cognitive requise pour analyser des phrases mal traduites; davantage de contrôle sur le processus; une possibilité que la machine exploite de nouvelles connaissances pour améliorer la traduction automatique au voisinage des segments pré-traduits, etc. De plus, dans un contexte multilingue, des difficultés communes peuvent être résolues simultanément pour de nombreuses langues. De tels scénarios s'adaptent donc parfaitement aux contextes de production standard, où l'un des principaux objectifs est de réduire le coût de l’intervention humaine et où les traductions sont généralement effectuées à partir d'une langue vers plusieurs langues à la fois. Dans ce contexte, nous nous concentrons sur la TA de revues systématiques en médecine. En considérant cet exemple, nous proposons une méthodologie indépendante du système pour la détection des difficultés de traduction. Nous définissons la notion de difficulté de traduction de la manière suivante : les segments difficiles à traduire sont des segments pour lesquels un système de TA fait des prédictions erronées. Nous formulons le problème comme un problème de classification binaire et montrons que, en utilisant cette méthodologie, les difficultés peuvent être détectées de manière fiable sans avoir accès à des informations spécifiques au système. Nous montrons que dans un contexte multilingue, les difficultés communes sont rares. Une perspective plus prometteuse en vue d'améliorer la qualité réside dans des approches dans lesquelles les traductions dans les différentes langues s’aident mutuellement à résoudre leurs difficultés. Nous intégrons les résultats de notre procédure de détection des difficultés dans un protocole de pré-édition qui permet de résoudre ces difficultés par pré-traduction. Nous évaluons le protocole dans un cadre simulé et montrons que la pré-traduction peut être à la fois utile pour améliorer la qualité de la TA et réaliste en termes d'implication des efforts humains. En outre, les effets indirects sont significatifs. Nous évaluons également notre protocole dans un contexte préliminaire impliquant des interventions humaines. Les résultats de ces expériences pilotes confirment les résultats obtenus dans le cadre simulé et ouvrent des perspectives encourageantes pour des tests ultérieures. / Machine Translation (MT) has made significant progress in the recent years and continues to improve. Today, MT is successfully used in many contexts, including professional translation environments and production scenarios. However, the translation process requires knowledge larger in scope than what can be captured by machines even from a large quantity of translated texts. Since injecting human knowledge into MT is required, one of the potential ways to improve MT is to ensure an optimized human-machine collaboration. To this end, many questions are asked by modern research in MT: How to detect where human assistance should be proposed? How to make machines exploit the obtained human knowledge so that they could improve their output? And, not less importantly, how to optimize the exchange so as to minimize the human effort involved and maximize the quality of MT output? Various solutions have been proposed depending on concrete implementations of the MT process. In this thesis we have chosen to focus on Pre-Edition (PRE), corresponding to a type of human intervention into MT that takes place ex-ante, as opposed to Post-Edition (PE), where human intervention takes place ex-post. In particular, we study targeted PRE scenarios where the human is to provide translations for carefully chosen, difficult-to-translate, source segments. Targeted PRE scenarios involving pre-translation remain surprisingly understudied in the MT community. However, such PRE scenarios can offer a series of advantages as compared, for instance, to non-targeted PE scenarios: i.a., the reduction of the cognitive load required to analyze poorly translated sentences; more control over the translation process; a possibility that the machine will exploit new knowledge to improve the automatic translation of neighboring words, etc. Moreover, in a multilingual setting common difficulties can be resolved at one time and for many languages. Such scenarios thus perfectly fit standard production contexts, where one of the main goals is to reduce the cost of PE and where translations are commonly performed simultaneously from one language into many languages. A representative production context - an automatic translation of systematic medical reviews - is the focus of this work. Given this representative context, we propose a system-independent methodology for translation difficulty detection. We define the notion of translation difficulty as related to translation quality: difficult-to-translate segments are segments for which an MT system makes erroneous predictions. We cast the problem of difficulty detection as a binary classification problem and demonstrate that, using this methodology, difficulties can be reliably detected without access to system-specific information. We show that in a multilingual setting common difficulties are rare, and a better perspective of quality improvement lies in approaches where translations into different languages will help each other in the resolution of difficulties. We integrate the results of our difficulty detection procedure into a PRE protocol that enables resolution of those difficulties by pre-translation. We assess the protocol in a simulated setting and show that pre-translation as a type of PRE can be both useful to improve MT quality and realistic in terms of the human effort involved. Moreover, indirect effects are found to be genuine. We also assess the protocol in a preliminary real-life setting. Results of those pilot experiments confirm the results in the simulated setting and suggest an encouraging beginning of the test phase.

La diversité des langues complexifie la tâche de communication entre les humains à travers les différentes cultures. La traduction automatique est un moyen rapide et peu coûteux pour simplifier la communication interculturelle. Récemment, laTraduction Automatique Neuronale (NMT) a atteint des résultats impressionnants. Cette thèse s'intéresse à la Traduction Automatique Neuronale Factorisé (FNMT) qui repose sur l'idée d'utiliser la morphologie et la décomposition grammaticale des mots (lemmes et facteurs linguistiques) dans la langue cible. Cette architecture aborde deux défis bien connus auxquelles les systèmes NMT font face. Premièrement, la limitation de la taille du vocabulaire cible, conséquence de la fonction softmax, qui nécessite un calcul coûteux à la couche de sortie du réseau neuronale, conduisant à un taux élevé de mots inconnus. Deuxièmement, le manque de données adéquates lorsque nous sommes confrontés à un domaine spécifique ou une langue morphologiquement riche. Avec l'architecture FNMT, toutes les inflexions des mots sont prises en compte et un vocabulaire plus grand est modélisé tout en gardant un coût de calcul similaire. De plus, de nouveaux mots non rencontrés dans les données d'entraînement peuvent être générés. Dans ce travail, j'ai développé différentes architectures FNMT en utilisant diverses dépendances entre les lemmes et les facteurs. En outre, j'ai amélioré la représentation de la langue source avec des facteurs. Le modèle FNMT est évalué sur différentes langues dont les plus riches morphologiquement. Les modèles à l'état de l'art, dont certains utilisant le Byte Pair Encoding (BPE) sont comparés avec le modèle FNMT en utilisant des données d'entraînement de petite et de grande taille. Nous avons constaté que les modèles utilisant les facteurs sont plus robustes aux conditions d'entraînement avec des faibles ressources. Le FNMT a été combiné avec des unités BPE permettant une amélioration par rapport au modèle FNMT entrainer avec des données volumineuses. Nous avons expérimenté avec dfférents domaines et nous avons montré des améliorations en utilisant les modèles FNMT. De plus, la justesse de la morphologie est mesurée à l'aide d'un ensemble de tests spéciaux montrant l'avantage de modéliser explicitement la morphologie de la cible. Notre travail montre les bienfaits de l'applicationde facteurs linguistiques dans le NMT. / Communication between humans across the lands is difficult due to the diversity of languages. Machine translation is a quick and cheap way to make translation accessible to everyone. Recently, Neural Machine Translation (NMT) has achievedimpressive results. This thesis is focus on the Factored Neural Machine Translation (FNMT) approach which is founded on the idea of using the morphological and grammatical decomposition of the words (lemmas and linguistic factors) in the target language. This architecture addresses two well-known challenges occurring in NMT. Firstly, the limitation on the target vocabulary size which is a consequence of the computationally expensive softmax function at the output layer of the network, leading to a high rate of unknown words. Secondly, data sparsity which is arising when we face a specific domain or a morphologically rich language. With FNMT, all the inflections of the words are supported and larger vocabulary is modelled with similar computational cost. Moreover, new words not included in the training dataset can be generated. In this work, I developed different FNMT architectures using various dependencies between lemmas and factors. In addition, I enhanced the source language side also with factors. The FNMT model is evaluated on various languages including morphologically rich ones. State of the art models, some using Byte Pair Encoding (BPE) are compared to the FNMT model using small and big training datasets. We found out that factored models are more robust in low resource conditions. FNMT has been combined with BPE units performing better than pure FNMT model when trained with big data. We experimented with different domains obtaining improvements with the FNMT models. Furthermore, the morphology of the translations is measured using a special test suite showing the importance of explicitly modeling the target morphology. Our work shows the benefits of applying linguistic factors in NMT.

This study focuses on and tries to identify areas where machine translation can be useful by examining translated fictional and non-fictional texts, and the extent to which these different text types are better or worse suited for machine translation.  It additionally evaluates the performance of the free online translation tool Google Translate (GT). The BLEU automatic evaluation metric for machine translation was used for this study, giving a score of 27.75 BLEU value for fictional texts and 32.16 for the non-fictional texts. The non-fictional texts are samples of law documents, (commercial) company reports, social science texts (religion, welfare, astronomy) and medicine. These texts were selected because of their degree of difficulty. The non-fictional sentences are longer than those of the fictional texts and in this regard MT systems have struggled. In spite of having longer sentences, the non-fictional texts got a higher BLUE score than the fictional ones. It is speculated that one reason for the higher score of non-fictional texts might be that more specific terminology is used in these texts, leaving less room for subjective interpretation than for the fictional texts. There are other levels of meaning at work in the fictional texts that the human translator needs to capture.

This diploma thesis focuses on machine translation (MT), which has been studied for a relatively long time in linguistics (and later also in translation studies) and which in recent years is at the forefront of the broader public as well. This thesis aims to explore the quality of machine translation outputs and the way it changes over time. The theoretical part first deals with the machine translation in general, namely basic definitions, brief history and approaches to machine translation, then describes online machine translation systems and evaluation methods. Finally, this part provides a methodological model for the empirical part. Using a set of texts translated with MT, the empirical part seeks to check how online machine translation systems deal with translation of different text-types and whether there is improvement of the quality of MT outputs over time. In order to do so, an analysis of text-type, semantics, lexicology, stylistics and pragmatics is carried out as well as a rating of the general applicability of the translation. The final part of this thesis compares and concludes the results of the analysis. With regard to this comparation, conclusions are made and general tendencies stated that have emerged from the empirical part of the thesis.