A study of context influences in Arabic-English language translation technologies

Social and cultural context is largely missing in current language translation systems. Dictionary based systems translate terms in a source language to an equivalent term in a target language, but often the translation could be inaccurate when context is not taken into consideration, or when an equivalent term in the target language does not exist. Domain knowledge and context can be made explicit by using ontologies, and ontology utilization would enable inclusion of semantic relations to other terms, leading to translation results which is more comprehensive than a single equivalent term. It is proposed that existing ontologies in the domain should be utilized and combined by ontology merging techniques, to leverage on existing resources to form a basis ontology with contextual representation, and this can be further enhanced by using machine translation techniques on existing corpora to improve the basic ontology to append further contextual information to the knowledge base. Statistical methods in machine translation could provide automated relevance determination of these existing resources which are machine readable, and aid the human translator in establishing a domain specific knowledge base for translation. Advancements in communication and technologies has made the world smaller where people of different regions and languages need to work together and interact.The accuracy of these translations are crucial as it could lead to misunderstandings and possible conflict. While single equivalent terms in a target language can provide a gist of the meaning of a source language term, a semantic conceptualisation provided by an ontology could enable the term to be understood in the specific context that it is being used

Improving the translation environment for professional translators

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

Marketing and Advertising Translation: Humans vs Machines in the field of cosmetics

This undergraduate thesis focuses on a very specific field of specialized translation: advertising and marketing translation. Indeed, the high degree of specialization involved in this activity provides a testing ground for a reconsideration of the importance of the human translator and a reformulation of their role. The constant development of new technologies creates ever more sophisticated translation programs, which in turn revives the long-standing machine vs human translation debate. The aim of this project is to conduct a practical exercise targeted at verifying whether specialization in translation always requires the supervision of humans equipped with the relevant linguistic knowledge and technical background, or whether, on the contrary, machine translation can at present provide valid enough results and a sufficient level of reliability.El presente Trabajo de Fin de Grado se centra en un campo muy concreto de la traducción especializada: la traducción para la publicidad y la mercadotecnia. De hecho, el alto grado de especialización que implica esta actividad proporciona un campo de pruebas para una reconsideración de la importancia del traductor humano y una reformulación de su papel. El desarrollo creciente e ininterrumpido de las nuevas tecnologías está produciendo programas de traducción cada vez más sofisticados, lo que a su vez reaviva el viejo debate que confronta la traducción humana y la traducción automática. El objetivo de este proyecto es llevar a cabo un ejercicio práctico destinado a verificar si la especialización en la traducción siempre requiere la supervisión de personas con la formación lingüística y los conocimientos técnicos pertinentes, o si, por el contrario, la traducción automática puede en la actualidad proporcionar por si sola resultados suficientes y un nivel suficiente de fiabilidad.Grado en Estudios Inglese

Design of English-Hindi Translation Memory for Efficient Translation

Developing parallel corpora is an important and a difficult activity for Machine Translation. This requires manual annotation by Human Translators. Translating same text again is a useless activity. There are tools available to implement this for European Languages, but no such tool is available for Indian Languages. In this paper we present a tool for Indian Languages which not only provides automatic translations of the previously available translation but also provides multiple translations, in cases where a sentence has multiple translations, in ranked list of suggestive translations for a sentence. Moreover this tool also lets translators have global and local saving options of their work, so that they may share it with others, which further lightens the task.Comment: Proceedings of National Conference in Recent Advances in Computer Engineering, 201

Hybrid Approach to English-Hindi Name Entity Transliteration

Machine translation (MT) research in Indian languages is still in its infancy. Not much work has been done in proper transliteration of name entities in this domain. In this paper we address this issue. We have used English-Hindi language pair for our experiments and have used a hybrid approach. At first we have processed English words using a rule based approach which extracts individual phonemes from the words and then we have applied statistical approach which converts the English into its equivalent Hindi phoneme and in turn the corresponding Hindi word. Through this approach we have attained 83.40% accuracy.Comment: Proceedings of IEEE Students' Conference on Electrical, Electronics and Computer Sciences 201