Vantaggi e limiti dell'integrazione tra sistemi di traduzione assistita e sistemi di traduzione automatica

Computer-assisted translation (or computer-aided translation or CAT) is a form of language translation in which a human translator uses computer software in order to facilitate the translation process. Machine translation (MT) is the automated process by which a computerized system produces a translated text or speech from one natural language to another. Both of them are leading and promising technologies in the translation industry; it therefore seems important that translation students and professional translators become familiar with this relatively new types of technology. Whether used together, not only might these two different types of systems reduce translation time, but also lead to a further improvement in the field of translation technologies. The dissertation consists of four chapters. The first one surveys the chronological development of MT and CAT tools, the emergence of pre-editing, post-editing and controlled language and the very last frontiers in this sector. The second one provide a general overview on the four main CAT tools that are used nowadays and tested hereto. The third chapter is dedicated to the experimentations that have been conducted in order to analyze and evaluate the performance of the four integrated systems that are the core subject of this dissertation. Finally, the fourth chapter deals with the issue of terminological equivalence in interlinguistic translation. The purpose of this dissertation is not to provide an objective and definitive solution to the complex issues that arise at any time in the field of translation technologies, this aim being well away from being achieved, but to supply information about the limits and potentiality that are typical of those instruments which are now essential to any professional translator.

Patent translation with CAT tools and Machine Translation

The aim of this essay, which focuses on patent translation, is to compare the use of Computer-Assisted Translation (CAT) and Machine Translation (MT). During my curricular internship at a specialized-translation agency called Centro Traduzioni Imolese, I was able to practice patent translation thanks to CAT tools like SDL Trados Studio, something I have never studied at university in Forlì. Nowadays, however, Machine Translation is widely used in patent translation as well, due to the vast number of technical terms and their repetitiveness in patents, so the machine can translate automatically and rapidly all repeated terms with the same word, thanks to the use of corpora and translation memories linked to the patent field. In the first chapter I will give a definition of what a patent is, and I will introduce the concept of patent literature; afterwards, I will illustrate the differences between Computer-Assisted Translation and Machine Translation used in patent translation. In the second chapter I will translate two portions of patent 102019000018530, via the Matecat online application, translating the first part with CAT and the second part with MT, then doing the same for the second portion selected from the patent. Finally, in the third chapter, I will analyse the two translations, comparing the results in order to discover which is the more efficient method for translating patents.

Machine Translation: Translators' Friend or Foe?

This paper discusses the impact of machine translation on the language industry, specifically addressing its effect on translators. It summarizes the history of the development of machine translation, explains the underlying theory that ties machine translation to its practical applications, and describes the different types of machine translation as well as other tools familiar to translators. There are arguments for and against its use, as well as evaluation methods for testing it. Internet and real-time communication are featured for their role in the increase of machine translation use. The potential that this technology has in the future of professional translation is examined. This paper shows that machine translation will continue to be increasingly used whether translators like it or not.

Machine Translation and the Disruption of Foreign Language Learning Activities

This study examines the question of how language teachers in a highly technologyfriendly university environment view machine translation and the implications that this has for the personal learning environments of students. It brings an activity-theory perspective to the question, examining the ways that the introduction of new tools can disrupt the relationship between different elements in an activity system. This perspective opens up for an investigation of the ways that new tools have the potential to fundamentally alter traditional learning activities. In questionnaires and group discussions, respondents showed general agreement that although use of machine translation by students could be considered cheating, students are bound to use it anyway, and suggested that teachers focus on the kinds of skills students would need when using machine translation and design assignments and exams to practice and assess these skills. The results of the empirical study are used to reflect upon questions of what the roles of teachers and students are in a context where many of the skills that a person needs to be able to interact in a foreign language increasingly can be outsourced to laptops and smartphones.

Machine translation and the disruption of foreign language learning activities

This study examines the question of how language teachers in a highly technology-friendly university environment view machine translation and the implications that this has for the personal learning environments of students. It brings an activity-theory perspective to the question, examining the ways that the introduction of new tools can disrupt the relationship between different elements in an activity system. This perspective opens up for an investigation of the ways that new tools have the potential to fundamentally alter traditional learning activities. In questionnaires and group discussions, respondents showed general agreement that although use of machine translation by students could be considered cheating, students are bound to use it anyway, and suggested that teachers focus on the kinds of skills students would need when using machine translation and design assignments and exams to practice and assess these skills. The results of the empirical study are used to reflect upon questions of what the roles of teachers and students are in a context where many of the skills that a person needs to be able to interact in a foreign language increasingly can be outsourced to laptops and smartphones.

Machine translation quality in an audiovisual context

The volume of Audiovisual Translation (AVT) is increasing to meet the rising demand for data that needs to be accessible around the world. Machine Translation (MT) is one of the most innovative technologies to be deployed in the field of translation, but it is still too early to predict how it can support the creativity and productivity of professional translators in the future. Currently, MT is more widely used in (non-AV) text translation than in AVT. In this article, we discuss MT technology and demonstrate why its use in AVT scenarios is particularly challenging. We also present some potentially useful methods and tools for measuring MT quality that have been developed primarily for text translation. The ultimate objective is to bridge the gap between the tech-savvy AVT community, on the one hand, and researchers and developers in the field of high-quality MT, on the other.

Towards a user-friendly webservice architecture for statistical machine translation in the PANACEA project

This paper presents a webservice architecture for Statistical Machine Translation aimed at non-technical users. A workfloweditor allows a user to combine different webservices using a graphical user interface. In the current state of this project,the webservices have been implemented for a range of sentential and sub-sententialaligners. The advantage of a common interface and a common data format allows the user to build workflows exchanging different aligners.

Shallow-transfer rule-based machine translation for Czech to Polish

This article describes the developmentof an Open Source shallow-transfer machine translation system from Czech to Polish in theApertium platform. It gives details ofthe methods and resources used in contructingthe system. Although the resulting system has quite a high error rate, it is still competitive with other systems.

Automatic acquisition of Named Entities for Rule-Based Machine Translation

This paper proposes to enrich RBMTdictionaries with Named Entities(NEs) automatically acquired fromWikipedia. The method is appliedto the Apertium English-Spanishsystem and its performance comparedto that of Apertium with and withouthandtagged NEs. The system withautomatic NEs outperforms the onewithout NEs, while results vary whencompared to a system with handtaggedNEs (results are comparable forSpanish to English but slightly worstfor English to Spanish). Apart fromthat, adding automatic NEs contributesto decreasing the amount of unknownterms by more than 10%.

Taking on new challenges in multi-word unit processing for Machine Translation

This paper discusses the qualitativecomparative evaluation performed on theresults of two machine translation systemswith different approaches to the processing ofmulti-word units. It proposes a solution forovercoming the difficulties multi-word unitspresent to machine translation by adopting amethodology that combines the lexicongrammar approach with OpenLogos ontologyand semantico-syntactic rules. The paper alsodiscusses the importance of a qualitativeevaluation metrics to correctly evaluate theperformance of machine translation engineswith regards to multi-word units.

A Widely Used Machine Translation Service and its Migration to a Free/Open-Source Solution : the Case of Softcatalà

Softcatalà is a non-profit associationcreated more than 10 years ago to fightthe marginalisation of the Catalan languagein information and communicationtechnologies. It has led the localisationof many applications and thecreation of a website which allows itsusers to translate texts between Spanishand Catalan using an external closed-sourcetranslation engine. Recently,the closed-source translation back-endhas been replaced by a free/open-sourcesolution completely managed by Softcatalà: the Apertium machine translationplatform and the ScaleMT web serviceframework. Thanks to the opennessof the new solution, it is possibleto take advantage of the huge amount ofusers of the Softcatalà translation serviceto improve it, using a series ofmethods presented in this paper. In addition,a study of the translations requestedby the users has been carriedout, and it shows that the translationback-end change has not affected theusage patterns.

Rule-based machine translation between Bulgarian and Macedonian

This paper describes the development of a two-way shallow-transfer rule-based machine translation system between Bulgarian and Macedonian. It gives an account of the resources and the methods used for constructing the system, including the development of monolingual and bilingual dictionaries, syntactic transfer rules and constraint grammars. An evaluation of thesystem's performance was carried out and compared to another commercially available MT system for the two languages. Some future work was suggested.

From Word Embeddings to Large Vocabulary Neural Machine Translation

Dans ce mémoire, nous examinons certaines propriétés des représentations distribuées de mots et nous proposons une technique pour élargir le vocabulaire des systèmes de traduction automatique neurale. En premier lieu, nous considérons un problème de résolution d'analogies bien connu et examinons l'effet de poids adaptés à la position, le choix de la fonction de combinaison et l'impact de l'apprentissage supervisé. Nous enchaînons en montrant que des représentations distribuées simples basées sur la traduction peuvent atteindre ou dépasser l'état de l'art sur le test de détection de synonymes TOEFL et sur le récent étalon-or SimLex-999. Finalament, motivé par d'impressionnants résultats obtenus avec des représentations distribuées issues de systèmes de traduction neurale à petit vocabulaire (30 000 mots), nous présentons une approche compatible à l'utilisation de cartes graphiques pour augmenter la taille du vocabulaire par plus d'un ordre de magnitude. Bien qu'originalement développée seulement pour obtenir les représentations distribuées, nous montrons que cette technique fonctionne plutôt bien sur des tâches de traduction, en particulier de l'anglais vers le français (WMT'14).

Syntactic Based Machine Translation from English to Malayalam

Due to the emergence of multiple language support on the Internet, machine translation (MT) technologies are indispensable to the communication between speakers using different languages. Recent research works have started to explore tree-based machine translation systems with syntactical and morphological information. This work aims the development of Syntactic Based Machine Translation from English to Malayalam by adding different case information during translation. The system identifies general rules for various sentence patterns in English. These rules are generated using the Parts Of Speech (POS) tag information of the texts. Word Reordering based on the Syntax Tree is used to improve the translation quality of the system. The system used Bilingual English –Malayalam dictionary for translation.

Handling OOV Words in Phrase-Based Statistical Machine Translation for Malayalam

Statistical Machine Translation (SMT) is one of the potential applications in the field of Natural Language Processing. The translation process in SMT is carried out by acquiring translation rules automatically from the parallel corpora. However, for many language pairs (e.g. Malayalam- English), they are available only in very limited quantities. Therefore, for these language pairs a huge portion of phrases encountered at run-time will be unknown. This paper focuses on methods for handling such out-of-vocabulary (OOV) words in Malayalam that cannot be translated to English using conventional phrase-based statistical machine translation systems. The OOV words in the source sentence are pre-processed to obtain the root word and its suffix. Different inflected forms of the OOV root are generated and a match is looked up for the word variants in the phrase translation table of the translation model. A Vocabulary filter is used to choose the best among the translations of these word variants by finding the unigram count. A match for the OOV suffix is also looked up in the phrase entries and the target translations are filtered out. Structuring of the filtered phrases is done and SMT translation model is extended by adding OOV with its new phrase translations. By the results of the manual evaluation done it is observed that amount of OOV words in the input has been reduced considerably