Abstract We present a new web-based CAT tool providing translators with a professional work environment, integrating translation memories, terminology bases, concordancers, and machine translation. The tool is completely developed as open source software and has been already successfully deployed for business, research and education. The MateCat Tool represents today probably the best available open source platform for investigating, integrating, and evaluating under realistic conditions the impact of new machine translation technology on human post-editing

A Cattelan

A Farina

A Martines

A Massidda

C Buck

D Lupinetti

F Blain

H Schwenk

Italy

L Barrault

M Cettolo

M Federico

M Negri

M Turchi Fondazione

N Bertoldi

P Koehn

Roma

Translated Srl

Trento (Italy) M. Trombetti Bruno Kessler

U Germann

CiteSeerX

Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: System Demonstrations,
pages 129–132, Dublin, Ireland, August 23-29 2014.
THE MATECAT TOOL
M. Federico and N. Bertoldi and M. Cettolo and M. Negri and M. Turchi
Fondazione Bruno Kessler, Trento (Italy)
M. Trombetti and A. Cattelan and A. Farina and
D. Lupinetti and A. Martines and A. Massidda
Translated Srl, Roma (Italy)
H. Schwenk and L. Barrault and F. Blain
Université du Maine, Le Mans (France)
P. Koehn and C. Buck and U. Germann
The University of Edinburgh (United Kingdom)
www.matecat.com
Abstract
We present a new web-based CAT tool providing translators with a professional work environ-
ment, integrating translation memories, terminology bases, concordancers, and machine transla-
tion. The tool is completely developed as open source software and has been already successfully
deployed for business, research and education. The MateCat Tool represents today probably the
best available open source platform for investigating, integrating, and evaluating under realistic
conditions the impact of new machine translation technology on human post-editing.
1 Introduction
The objective of MateCat1 is to improve the integration of machine translation (MT) and human transla-
tion within the so-called computer aided translation (CAT) framework. CAT tools represent nowadays the
dominant technology in the translation industry. They provide translators with text editors that can man-
age several document formats and suitably arrange their content into text segments ready to be translated.
Most importantly, CAT tools provide access to translation memories (TMs), terminology databases, con-
cordance tools and, more recently, to machine translation (MT) engines. A TM is basically a repository
of translated segments. During translation, the CAT tool queries the TM to search for exact or fuzzy
matches of the current source segment. These matches are proposed to the user as translation sugges-
tions. Once a segment is translated, its source and target texts are added to the TM for future queries. The
integration of suggestions from an MT engine as a complement to TM matches is motivated by recent
studies (Federico et al., 2012; Green et al., 2013; Läubli et al., 2013), which have shown that post-editing
MT suggestions can substantially improve the productivity of professional translators. MateCat lever-
ages the growing interest and expectations in statistical MT by advancing the state-of-the-art along three
directions:
• Self-tuning MT, i.e. methods to train statistical MT for specific domains or translation projects;
• User adaptive MT, i.e. methods to quickly adapt statistical MT from user corrections and feedback;
• Informative MT, i.e. supply more information to enhance users’ productivity and work experience.
Research along these three directions has converged into a new generation CAT software, which is
both an enterprise level translation workbench (currently used by several hundreds of professional trans-
lators) as well as an advanced research platform for integrating new MT functions, running post-editing
This work is licensed under a Creative Commons Attribution 4.0 International License. Page numbers and proceedings
footer are added by the organizers. License details: http:// creativecommons.org/licenses/by/4.0/.
1MateCat, acronym of Machine Translation Enhanced Computer Assisted Translation, is a 3-year research project (11/2011-
10/2014) funded by the European Commission under FP7 (grant agreement no 287688). The project consortium is led by FBK
(Trento, Italy) and includes the University of Edinburgh (United Kingdom), Université du Maine (Le Mans, France), and
Translated Srl (Rome, Italy).
129
Figure 1: The MateCat Tool editing page.
experiments and measuring user productivity. The MateCat Tool, which is distributed under the LGPL
open source license, combines features of the most advanced systems (either commercial, like the pop-
ular SDL Trados Workbench,2 or free like OmegaT3) with new functionalities. These include: i) an
advanced API for the Moses Toolkit,4 customizable to languages and domains, ii) ease of use through a
clean and intuitive web interface that enables the collaboration of multiple users on the same project, iii)
concordancers, terminology databases and support for customizable quality estimation components and
iv) advanced logging functionalities.
2 The MateCat Tool in a Nutshell
Overview. The MateCat Tool runs as a web-server accessible through Chrome, Firefox and Safari. The
CAT web-server connects with other services via open APIs: the TM server MyMemory5, the commer-
cial Google Translate (GT) MT server, and a list of Moses-based servers specified in a configuration file.
While MyMemory’s and GT’s servers are always running and available, customized Moses servers have
to be first installed and set-up. Communication with the Moses servers extends the GT API in order to
support self-tuning, user-adaptive and informative MT functions. The natively supported document for-
mat of MateCat Tool is XLIFF,6 although its configuration file makes it possible to specify external file
converters. The tool supports Unicode (UTF-8) encoding, including non latin alphabets and right-to-left
languages, and handles texts embedding mark-up tags.
How it works. The tool is intended both for individual translators or managers of translation projects
involving one or more translators. A translation project starts by uploading one or more documents and
specifying the desired translation direction. Then the user can optionally select a MT engine from an
available list and/or a new or existing private TM in MyMemory, by specifying its private key. Notice
that the public MyMemory TM and the GT MT services are assumed by default. The following step is
the volume analysis of the document, which reports statistics about the words to be actually translated
based on the coverage provided by the TM. At this stage, long documents can be also split into smaller
portions to be for instance assigned to different translators or translated at different times. The following
step starts the actual translation process by opening the editing window. All source segments of the
2http://www.translationzone.com/
3http://www.omegat.org/
4http://www.statmt.org/moses/
5http://mymemory.translated.net
6http://docs.oasis-open.org/xliff/v1.2/os/xliff-core.html
130
document and their corresponding target segments are arranged side-by-side on the screen. By selecting
one segment, an editing pane opens (Figure 1) including an editable field that is initialized with the best
available suggestion or with the last post-edit. Translation hints are shown right below together with
their origin (MT or TM). Their ranking is based on the TM match score or the MT confidence score. MT
hints with no confidence score are assigned a default score. Tag consistency is automatically checked
during translation and warnings are possibly shown in the editing window. An interesting feature of the
MateCat Tool is that each translation project is uniquely identified by its URL page which also includes
the currently edited segment. This permits for instance more users to simultaneously access and work on
the same project. Moreover, to support simultaneous team work on the same project, translators can mark
the status (draft, translated, approved, rejected) of each segment with a corresponding color (see Figure
1, right blue bar). The user interface is enriched with search and replace functions, a progress report at
the bottom of the page, and several shortcut commands for the skilled users. Finally, the tool embeds a
concordance tool to search for terms in the TM, and a glossary where each user can upload, query and
update her terminology base. Users with a Google account can access a project management page which
permits then to manage all their projects, including storage, deletion, and access to the editing page.
MT support. The tool supports Moses-based servers able to provide an enhanced CAT-MT commu-
nication. 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, properly
handles text segments including tags, and instantly adapts from the post-edits performed by each user
(Bertoldi et al., 2013).
Edit Log. 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 2) 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.
3 Applications.
The MateCat Tool has been exploited by the MateCat project to investigate new MT functions (Bertoldi
et al., 2013; Cettolo et al., 2013; Turchi et al., 2013; Turchi et al., 2014) and to evaluate them in a real
professional setting, in which translators have at disposal all the sources of information they are used
to work with. Moreover, taking advantage of its flexibility and ease of use, the tool has been recently
exploited for data collection and education purposes (a course on CAT technology for students in trans-
lation studies). An initial version of the tool has also been leveraged by the Casmacat project7 to create
a workbench (Alabau et al., 2013), particularly suitable for investigating advanced interaction modalities
such as interactive MT, eye tracking, and handwritten input. Currently the tool is employed by Trans-
lated for their internal translation projects and is being tested by several international companies, both
language service providers and IT companies. This has made 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.
7http://www.casmacat.eu
131
Figure 2: The MateCat Tool edit log page.
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