MateCat is a web-based computer-assisted translation (CAT) tool, released as open-source software under the Lesser General Public License (LGPL).
MateCat is a web-based computer-assisted translation (CAT) tool, released as open-source software under the Lesser General Public License (LGPL).
MateCat ("Machine Translation Enhanced Computer Assisted Translation") is a 3-year research project (Nov 2011 – Oct 2014) funded by the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement No. 287688. [1] It has received over €2,500,000 of European funds. [2]
The project consortium was led by FBK (Fondazione Bruno Kessler), an international research center based in Trento, Italy, and included Translated, an AI-based language solution provider founded by Marco Trombetti and Isabelle Andrieu, Université du Maine, and the University of Edinburgh.
CAT tools provide access to translation memories (TMs), terminology databases, concordance tools and, more recently, to machine translation (MT) engines. The integration of suggestions from an MT engine as a complement to TM matches is motivated by recent studies, [3] [4] [5] which have shown that post-editing MT suggestions improves the level of accuracy in translations.
MateCat facilitates editing machine translation results and manages the localization workflow. It leverages knowledge of field-specific language (for example, legal terminology) to improve translation suggestions, and also uses machine learning to automatically improve suggestions over time. [6] It's designed to function both as a translation workbench product and as a research platform for integrating new MT functions, running post-editing experiments, and measuring user productivity.
MateCat runs as a web server that connects with other services via open APIs: the TM service MyMemory, [7] the commercial Google Translate (GT) service, ModernMT, and a list of Moses-based [8] services specified in a configuration file. While MyMemory and GT are always available, Moses servers have to be installed and set-up. Moses allows MateCat to extend the GT API to support self-tuning, user-adaptive, and informative MT functions. The open-source version of MateCat natively supports the XLIFF [9] file format, but converters can be configured to support other formats. The tool supports Unicode (UTF-8) encoding, including non-Latin alphabets and right-to-left languages, and handles texts that embed mark-up tags. It supports concordances, terminology databases, and customizable quality estimation components, and provides an API for the Moses Toolkit that can be customized to languages and domains.
The tool supports Moses-based servers able to provide an enhanced CAT-MT communication. In particular, the GT API is augmented with feedback information provided to the MT engine every time a segment is post-edited as well as enriched MT output, including confidence scores, word lattices, etc. The developed MT server supports multi-threading to serve multiple translators, handles text segments including tags and adapts from the post-edits performed by each user [10]
MateCat also provides suggestions by MT which are consistent with respect not only to the already edited segments but also, in theory, to the whole document. This context information will be embedded in the statistical models and should enable better disambiguation, for instance, between lexical alternatives. The context-based models will combine information about recurring terms and expressions extracted during the document analysis with the corresponding chosen and confirmed translations as soon as they become available. In particular, translation constraints related to inter-sentence and intra-sentence anaphoric expressions, to syntactic concordances, and to lexical coherence will be taken into account by means of specific statistical models.
The core components of traditional MT systems, that is, the translation and the language models, are generally static: they never change after an initial training phase. This means that they are unsuitable for a dynamic environment like the one that MateCat is designing for translators. In order to model the dynamic changes depicted in the two previous tasks, MateCat developed innovative data-structures that can be rapidly and effectively updated as soon as a new translation is supplied by the user, and innovative, efficient algorithms for performing this adaptation in such a way that the whole process takes place in real time and is transparent to the translator. Moreover, efficiency will be improved by taking advantage of single CPU multithreading, as well as distributed computing facilities running on private clusters or computer clouds.
During post-editing the tool collects timing information for each segment, which is updated every time the segment is opened and closed. Moreover, for each segment, information is collected about the generated suggestions and the one that has actually been post-edited. This information is accessible at any time through a link in the Editing Page, named Editing Log. The Editing Log page (Figure 1) shows a summary of the overall editing performed so far on the project, such as the average translation speed and post-editing effort and the percentage of top suggestions coming from MT or the TM. Moreover, for each segment, sorted from the slowest to the fastest in terms of translation speed, detailed statistics about the performed edit operations are reported. This information, with even more details, can be also downloaded as a CSV file to perform a more detailed post-editing analysis. While the information shown in the Edit Log page is very useful to monitor progress of a translation project in real time, the CSV file is a fundamental source of information for detailed productivity analyses once the project is ended.
MateCat has been used by the MateCat project to investigate new MT functions [11] and to evaluate them in a real professional setting, in which translators have at their disposal all the sources of information they are used to working with. Moreover, taking advantage of its flexibility and ease of use, the tool has been recently used for data collection and education purposes (a course on CAT technology for students in translation studies). An initial version of the tool has also been leveraged by the CasmaCat project [12] to create a workbench, [13] particularly suitable for investigating advanced interaction modalities such as interactive MT, eye tracking, and handwritten input. Currently the tool is employed by the translation agency Translated for their internal translation projects and is being tested by several international companies, both language service providers and IT companies. This has made it possible to collect continuous feedback from hundreds of translators, which, besides helping us to improve the robustness of the tool, is also influencing the way new MT functions will be integrated to supply the best help to the final user.
Machine translation is use of either rule-based or probabilistic machine learning approaches to translation of text or speech from one language to another, including the contextual, idiomatic and pragmatic nuances of both languages.
A translation memory (TM) is a database that stores "segments", which can be sentences, paragraphs or sentence-like units that have previously been translated, in order to aid human translators. The translation memory stores the source text and its corresponding translation in language pairs called “translation units”. Individual words are handled by terminology bases and are not within the domain of TM.
A parallel text is a text placed alongside its translation or translations. Parallel text alignment is the identification of the corresponding sentences in both halves of the parallel text. The Loeb Classical Library and the Clay Sanskrit Library are two examples of dual-language series of texts. Reference Bibles may contain the original languages and a translation, or several translations by themselves, for ease of comparison and study; Origen's Hexapla placed six versions of the Old Testament side by side. A famous example is the Rosetta Stone, whose discovery allowed the Ancient Egyptian language to begin being deciphered.
Computer-aided translation (CAT), also referred to as computer-assisted translation or computer-aided human translation (CAHT), is the use of software, also known as a translator, to assist a human translator in the translation process. The translation is created by a human, and certain aspects of the process are facilitated by software; this is in contrast with machine translation (MT), in which the translation is created by a computer, optionally with some human intervention.
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Google Translator Toolkit was an online computer-assisted translation tool (CAT)—a web application designed to permit translators to edit the translations that Google Translate automatically generated using its own and/or user-uploaded files of appropriate glossaries and translation memory. The toolkit was designed to let translators organize their work and use shared translations, glossaries and translation memories, and was compatible with Microsoft Word, HTML, and other formats.
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Marco Trombetti is an Italian computer scientist, entrepreneur, investor, and ocean sailor. He co-founded Translated, a pioneer of artificial intelligence in the language industry. Through Translated, he helped develop the first AI-powered open-source Computer Assisted Translation (CAT) tool, Matecat, which also introduced the first adaptive machine translation system. He is considered one of the most influential leaders and innovators in the language industry. His research on progress toward the language singularity, presented during a keynote at the Association for Machine Translation in the Americas (AMTA) conference in 2022, has provided invaluable insights into the field of artificial intelligence.