![]() Users can review their translation and make any change to correct possible mistakes. Post-editing Machine Translation process These predictions help not only professional translators, but also novice translators who do not know the vocabulary and people without knowledge of the foreign language. The user's choices are scored in the database to be used in future translations. The most likely prediction is the result of previous matches in the database. The database is made of amounts of pairs of translated texts and translations. Predictions are accepted by clicking on them and the system updates the election to the user input. Predictions for these units appear in a box, and the most likely suggestion has a different colour in the highest part of the table. The text is divided into sentences, which are also divided into smaller units. ![]() ![]() The prediction table is displayed by clicking the edit icon. Once the text is uploaded, users can see the result of the machine translation and edit the text based on the predictions. Preliminary studies about CAITRA suggest that users usually accept 50-80% of predictions generated by the system. The acceptance of suggestions depends on the pair of languages and the complexity of the text. Once the user accepts a suggestion, a new one is displayed as well the typing of a new segment. This computation takes place at the server and is implemented in C++, as Philipp Koehn explains. ![]() The prediction is the optimal completion path that matches the user input with (a) minimal string edit distance and (b) highest sentence translation probability. During the user interaction, Caitra quickly matches user input against a graph using a string edit distance measure. The suggestions and user actions are stored in a large database. This model also makes it easier for the user to read the predictions. These predictions are provided in short phrases, according to the statistical phrase-based translation model. The statistical translation system is followed to generate the predictions for translation. This process is similar to the auto-completion tool used in several office programs. The human translators may choose one of them or provide their own translation if they do not like the offered translations. The Trans-Type project (Langlais et al., 2000) has done an investigation about Interactive Machine Translation, consisting of sentence-segment translation aided by a CAT tool, which suggests several different options for the translation. The segment for translation is the sentence and so Caitra works with only one sentence at the same time. Once the process is finished, translators have multiple options of assistance, presented in an interface. The process may last a few minutes, and Caitra will find different options for the translation, one of them is taken by default. Caitra processes the text as the user clicks the "Upload" icon. ![]() The user inputs text into the provided text box. The tool is provided online by the School of Informatics as a study of the user’s interaction with the tool, as well as the ability for members suggest additional features and fixes to the program. C++ is integrated to improve the speed of the process of translation suggestions. The machine translation back-end is powered by the statistical sentence-based MT, Moses (Koehn et al., 2007). The online platform uses Ajax-style Web 2.0 technologies (Raymond, 2007) connected to a MySQL database-driven back-end. The School of Informatics and the Machine Translation Group of the University of Edinburgh, created a research program, CAITRA, to analyze the benefits of different types of MTs and to explore the interaction between the machine and the user in order to develop new CAT tools.Ĭaitra is programmed with an open-source web framework, Ruby on Rails (Thomasand Hansson, 2008). Tools with post-edition facilities have also been developed as an intermediate field between typical MT and human translators in order to integrate MT and human translation and to achieve the desired results. This is, however, not necessarily suitable for professional translators. This translation tool would suggest different translations for a segment while providing the translator an opportunity to accept the suggested translation or overwrite it with their own translation, which in turn would trigger new potential translations to the tool. The Trans-Type project (Langlais et al., 2000) gave a pioneer approach to the MT as an aid to human translators. Professional translators usually require advanced machine translation tools to make their work easier and to give a higher quality translation to their clients. Machine Translation (MT) systems are typically used by readers who do not need a thorough translation and want quick access to the foreign language.
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