User:Luisa.jurado/Caitra (University of Edinburg translation tool)

Caitra is a translation tool developed by the University of Edinburgh. This Computer Assisted Tool or CAT tool is provided from an online platform, accessed from http://tool.statmt.org/ or http://www.caitra.org. It´s based on the AJAX Web.2 technologies and the Moses decoder. This web page of this tool is implemented with Ruby on Rails, an open source web framework, and C++, a statistically typed, multi-paradigm programming language. Caitra helps the human translators by offering suggestions and alternative translations. The translation process is simplified and faster.

Introduction
Machine Translation (MT) systems are usually used by readers who do not need a quality translation; they want a fast access to the foreign language. On the other hand, professional translators need more advanced Machine Translation tools to make their work easier and elaborating a high-quality translation for their clients. In the last years, MT has experienced a big development, but this MT is not always suitable for professional translator, because a simple MT would not aid translators, it would be only an extra-work. However, tools with post-edition facilities have been developed as an intermediate field between typical MT and human translators, in order to integrate MT and human translation and achieving a successful result. The Trans-Type project (Langlais et al., 2000) gave a pioneer approach to the MT as a help to human translators. The translation tool would suggest different translations for a segment and the translator may accept them or overwrite their own translation, which triggers new possible translations to the tool. The School of Informatics and the Machine Translation Group of the University of Edinburgh has created a research program, CAITRA, to analyze the benefits of different types of MT and explore the interaction between the machine and the user, in order to develop new CAT tools.

Properties
Caitra is programmed with an open-source web framework, Ruby on Rails (Thomasand Hansson, 2008). The online platform uses Ajax-style Web 2.0 technologies (Raymond, 2007) connected to a MySQL database-driven back-end. The machine translation back-end is powered by the statistical sentence-based MT, Moses (Koehn et al., 2007). C++ programming language is used to improve the speed of the translation suggestions The tool is provided online in order to make a wide research about this type of Machine Translation and obtain an advanced study of the user’s interaction with the tool. Moreover, the online feature allows the translation community to access to tool and know their opinions. You can access to Caitra web provider at http://www.caitra.org/

A simple text box is the link between the user and the tool. Caitra processes the text which is typed in the box by clicking the “Upload” icon. The process may last a few minutes, and Caitra will find different options for the translation, one of them is taken by default. Once the process is finished, translators have multiple options of assistance, presented in an interface. The segment for translation is the sentence and so Caitra works with only one sentence at the same time.

Interactive Machine translation
The Trans-Type project (Langlais et al., 2000) has done a deep investigation about Interactive Machine Translation, consisting of sentence-segment translation aided by a CAT tool, which suggests several different options for the translation. The human translators may choose one of them or typing their own translation if they do not like the offered translations. This process is similar to the auto-completion which is used in a lot of office programs.

The statistical translation system is followed to generate the predictions for translation. These predictions are provided in short phrases, according to the statistical phrase-based translation model. In addition, this model helps the user not to overload their sight, by using a few words at time. University of Edinburgh is still investigating the proper length for these suggestions but it has not been developed yet. At the moment, short phrases are used and they are more useful and not distractive for the users. The suggestions and the user actions are stored in a large data base. During the user interaction, Caitra quickly matches user input against the graph using a string edit distance measure. The prediction is the optimal completion path that matches the user input with (a) minimal string edit distance and (b) highest sentence translation probability. This computation takes place at the server and is implemented in C++, as Philipp Koehn explains1. Once the user accepts a suggestion, a new one is displayed as well the typing of a new segment. This process is very fast, it lasts less than a second. The acceptance of suggestions depends on the pair of languages and the difficulty of the text. Preliminary studies about CAITRA suggest that users usually accept 50-80% of predictions generated by the system.

Translation process
One the text is uploaded and after a few minutes wait, users can visualize the result of the machine translation and edit the text on the basis of the predictions. The prediction table is displayed by clicking the edit icon. The text is divided into sentences which are also divided into smaller units. Predictions for these units appear in a box, and the most likely suggestion has a different colour in the highest part of the table. Predictions are accepted by clicking on them and the system updates the election to the user input. The database is made of amounts of pairs of translated texts and translations. The most likely prediction is the result of previous matches in the data base. The users choices are scored in the data base to be used in future translations. These predictions help not only professional translators, but also novice translators who do not know the vocabulary and people who has no knowledge about the foreign language.

Post- editing Machine Translation process
Users can review their translation and make any change to correct possible mistakes. The changes appear in the output display.

User´s activity
Caitra stored in the data base the time users need to accept a prediction or writing their own translation. The actions have different importance for the future predictions depending on the user´s actions and in the time they need to perform their translation. Every action, pause or movement is relevant in order to improve future translations.