TransType2 – An Innovative Computer-Assisted Translation System 
José Esteban and José Lorenzo 
Atos Origin 
Albarracín 25 
28037 Madrid, Spain 
jfernando.esteban@atosorigin.com 
jose.lorenzo@atosorigin.com 
Antonio S. 
Valderrábanos 
Bitext.com 
General Oráa 3 
28006 Madrid, Spain 
asv@bitext.com 
Guy Lapalme 
RALI Laboratory 
Université de Montréal 
C.P. 6128, Succ Centreville 
Montréal, Québec 
Canada H3C 3J7 
lapalme@iro.umontreal.ca 
 
Abstract 
TT2 is an innovative tool for speeding up and 
facilitating the work of translators by 
automatically suggesting translation 
completions. Different versions of the system 
are being developed for English, French, 
Spanish and German by an international team 
of researchers from Europe and Canada. Two 
professional translation agencies are currently 
evaluating successive prototypes. 
1 Introduction 
TransType2 (TT2)
1
 is an innovative tool for 
speeding up and facilitating the work of translators 
by automatically suggesting translation 
completions. The system uses probabilistic 
translation and language models to calculate 
completions that are compatible with translator's 
input and, furthermore, revises its suggestions in 
real time with each new character the translator 
enters. If the system provides a correct suggestion, 
the translator has only to accept it, thereby saving 
time in producing the target text. Otherwise, the 
translator ignores the system's suggestions and 
continues to type his or her intended translation. 
TT2 is based on a new Machine Assisted 
Translation paradigm that sits between fully 
automatic MT and translation memory in order to 
significantly increase translator productivity on 
non-repetitive texts. TT2 is unique in the way in 
which it combines the strengths of MT technology 
with the competence of the human translator.  
The project is an extension of the TransType 
project that was developed from 1997 to 2000 by 
the RALI at Université de Montréal (Foster 1997, 
Langlais 2002), which demonstrated the interest of 
target text mediated computer aided translation. 
 
                                                      
1
 For further details, see http://tt2.sema.es 
Different versions of the system are being 
developed for English, French, Spanish and 
German (with English as the pivot). To ensure that 
TT2 corresponds to translators’ needs, two 
professional translation agencies are currently 
evaluating successive prototypes. To date, 
translation technology has not been able to keep 
pace with the demand for high-quality translation. 
TT2 has the ability to significantly increase 
translator productivity and thus has enormous 
commercial potential. 
TT2 is a RTD project funded by the European 
Commission under the Information Society 
Technologies Programme and includes five 
European partners: 
Atos Origin (Spain): administrative and 
technical coordinator, system design and 
integration.  
Lehrstuhl für Informatik VI, Computer 
Science Department, RWTH Aachen - University 
of Technology (Germany): statistical translation, 
speech recognition. 
Instituto Tecnológico de Informática, 
Universidad Politécnica de Valencia, (Spain): 
finite-state techniques for translation and speech 
recognition. 
Xerox Research Centre Europe, Grenoble 
(France): corpus provider and statistical translation 
modeling. 
Celer Soluciones, Madrid (Spain): evaluation in 
the operational context of a translation bureau. 
And two Canadian partners: 
RALI Laboratory, University of Montreal 
(Canada): user-interface, statistical modeling, 
evaluation coordination. 
Société Gamma, Ottawa (Canada): evaluation in 
the operational context of a translation bureau. 
 
Figure 1. User-view of TT2 with the source text on the left highlighting the sentence under translation. The 
translator types in the right pane in which TT2 suggests completions that appear in the menu in real-time. 
Completions can be accepted either by clicking an item from the menu or by the keyboard. This picture 
displays in red (appearing in gray in black and white) characters that have been suggested and accepted by 
the translator. 
 
2 TT2 as seen by a translator 
TransType is a tool that observes a translator as 
he or she is typing, tries to predict what will be 
typed next and displays its predictions to the user. 
The translator can incorporate these suggestions 
into the current target text if they are useful, or 
simply ignore them by continuing typing.  The 
system will then adapt itself to the new text typed 
by the translator. The suggestions can potentially 
improve a translator's productivity both by 
speeding up the keying in of the target text and by 
contributing to the translation process itself. If the 
underlying machine translation technology is good 
enough, TransType2's contributions may reduce 
the need to consult conventional tools such as a 
bilingual dictionary, term bank, or translation 
memory. 
The user interface (Figure 1) allows a real-time 
interaction with the output of the 
translation/language model to help a translator 
produce a translation. TransType2's main window 
is divided into two panes, one containing the 
source text and another containing the target text. 
The panes are displayed side by side, with their 
contents divided into aligned segments. They are 
also synchronized, so that scrolling one moves the 
other in parallel. Many aspects of the main 
window's behavior and appearance, such as the 
orientation of the source and target panes, can be 
changed using the commands accessible from the 
menu or keyboard shortcuts. 
The source pane is read-only in which the only 
operation allowed is the selection of a new 
sentence that triggers a new translation in the target 
window. The target window is a normal text 
editing window, except that after each character 
typed by the user, the system displays a pop-up 
menu of suggestions for completing the current 
input. If the user types a return or a tab, this 
suggestion is inserted in the text. Suggestions can 
be scrolled up or down with arrow keys or selected 
with the mouse. At initialization time, the user 
selects the prediction engine to be used according 
to one of six source-to-target translation pairs and 
one of the following domains: technical manuals, 
European Community official documents and 
official reports of the debates of the House of 
Commons of Canada (Hansards). 
3 System Architecture 
The TT2 system consists of two major 
subsystems that interact closely: 
user interface (UI), written in Java, provides the 
typing and pointing modalities; a second UI 
supplements those with speech for operating the 
prototype via short commands uttered by the user . 
The user interface also produces a trace of all user-
actions that can later be replayed by a special 
program or analyzed in order to evaluate the 
effectiveness of TransType2 both in terms of 
number of keystrokes needed for typing a 
translation and the various patterns of use. 
prediction engine (PE), written in C/C++, of 
which there are multiple realizations available, 
several per language pair and specific domain 
(either technical documentation, EC official 
documents or Hansards). The translation engines 
developed by research partners are: 
RALI (French↔English) is a maximum-entropy 
minimum-divergence translation model (Foster 
2000) that proposes multiple completions for the 
next few words. 
ITI (French↔English, Spanish↔English) are 
based on finite-state techniques (Cubel et al. 2003) 
and suggest a single completion of a whole 
sentence. 
RWTH (French↔English, Spanish↔English, 
German↔English) are statistical based (Och et al. 
2003) and suggest a single completion of a whole 
sentence. 
The main communications between the UI and 
the PE are the following: 
1. To initialize the PE, the UI calls a generic create 
method API function with the appropriate 
parameters required by each PE and checks its 
successful completion. 
2. Once the user has selected the file he/she wants 
to work with, the UI produces a list of text 
segments (sentences) and displays them in the 
source text pane of the interface. 
3. The selection of a source sentence is 
communicated to the PE by the UI. The sentence 
becomes the source text context prediction for 
the PE until the user selects another sentence. 
4. The UI communicates to the PE every single 
modification of the target text: insertion/removal 
of a new character (letter, digit, punctuation sign 
or white space) and cursor movements within the 
target text. The UI communicates left-right one-
character-at-a-time movements in the target text 
area. However, the PE does not take into account 
the text to the right of the cursor for making its 
predictions. 
5. In response to the request, the PE initiates the 
search for completions that are eventually re-
turned to the UI for their display. 
6. As part of the general exit procedure, the UI calls 
a generic destroy method API function with the 
appropriate parameters required by each PE and 
checks its successful completion. 
All communication exchanges between the UI 
and the PE are initiated by the UI, while the PE is 
in charge of responding by doing some actual 
work. This is particularly the case in 5 (producing 
a list of completions), while the others are more of 
an informative nature (cases 3 and 4) or can hardly 
considered communication exchanges at all: cases 
1, 2 (loading a text file and producing a list of 
sentences) and 6 (termination). 
Prediction engines and the speech recognizers 
are developed and tested under an operating 
platform (Linux) different than the one chosen for 
user testing (MS Windows). This duality implies 
that prediction engines and speech recognizers, 
while developed under Linux, should be able to 
run under Windows. The users (i.e. the two 
translation bureaus) voiced early in the project that 
TT2 system should run at least under Windows, 
although preferably it should also run under Linux. 
TT2 runs currently on both platforms, the 
dissemination and awareness of the TT2 prototype 
are broader, and go further than the initial 
objectives proposed inside the IST project. 
Given that developers of the prediction engines 
and speech recognizers were in favor of using 
C/C++ as their principal programming language, 
two practical alternatives were discussed: 
• Write code without operating platform 
dependencies and according to standards, that 
would allow compilers for both platforms to 
build functionally equivalent binary versions. 
• Employ tools that lessen to a certain extent the 
requirement of written C/C++ platform 
independent code, while allowing the porting of 
code from the Linux to the Windows platform. 
This was the preferred option and the three PE’s 
actually make use of one of such tool: Cygwin
2
. 
Cygwin provides a C/C++ compiler for the 
Windows platform and a library (cygwin1.dll) 
that gives support to Linux/Unix operating 
system services under the Windows 
environment. 
The partners responsible for developing the user 
interface have opted for JAVA as the programming 
language because of its graphical user capabilities, 
in particular its text components, which are fully 
configurable and compatible with external C/C++ 
programs. This option solves the portability 
problem, since the resulting code will run under 
any JAVA-enabled operating system.  
                                                      
2
 http://www.cygwin.com/ 
4 System requirements 
Generally speaking, running the TT2 system 
demands a high-end personal computer or work-
station in order to be able to provide translation 
completions in real-time and also to be able to 
incorporate multi-modal user input.  
The minimum user equipment is a high-end 
personal computer running under Windows with a 
minimum of 1GB of RAM; however, 2 GB of 
RAM and Windows XP Professional operating 
system is preferable. If a Linux operating system is 
used, the kernel version must be 2.4.20 or higher. 
It is also required to have installed the Java 2 
Runtime Environment, preferably version 
1.3.1_09. To produce the PE, cygwin1.dll version 
1.5.5-1 is required. 
The interface requirements of both scenarios 
include standard keyboard and mouse equipment; 
video display capable of resolutions of 1024x768 
pixels or higher and voice input hardware 
(microphone, a headset preferably, and sound card) 
if the optional speech recognition module is used.  
5 Evaluation 
TT2 is based on the premise that we can improve 
the productivity of translators by reducing the 
number of keystrokes needed for entering a 
translation. Professionals at two translation bureaus 
are currently testing the prototypes. Even though 
translators are not used to working with this kind 
of environment, some of them need about 50% less 
keystrokes to enter a translation and can thus 
produce a translation faster.  Many user interface 
improvements suggested by the translators will be 
included in the next prototypes. 
6 Conclusion 
TT2 is the outcome of a successful cooperation 
between European countries and Canada to 
develop an innovative approach to machine aided 
translation. It is based on advances in statistical 
machine translation research and on a seamless 
integration in a word processing environment of 
the same type as the one currently used by 
translators. 
7 Acknowledgements 
TT2 is a RTD project funded by the European 
Commission under the Information Society 
Technologies Programme (IST-2001-32091). In 
Canada it is funded by the National Science and 
Engineering Research Council and the Ministère 
du Développement Économique et Régional du 
Québec (Mission Recherche). 
References  
E. Cubel, J. González, A. Lagarda, F. Casacuberta, 
A. Juan and E.Vidal. Adapting finite-state 
translation to the TransType2 project. 
Proceedings of the 8th International Workshop 
of the European Association for Machine 
Translation and the 4th Controlled Language 
Applications Workshop Dublin City University 
Joint Conference, Ireland, 2003. 
Foster G., Isabelle P., Plamondon P. Target-Text 
Mediated Interactive Machine Translation, 
Machine Translation, 12:1-2, 175-194, 1997. 
Foster G., A Maximum Entropy / Minimum 
Divergence Translation Model, Proceedings of 
the 38th Annual Meeting of the Association for 
Computational Linguistics, pp. 37-42, Hong-
Kong, October 2000. 
Philippe Langlais, Guy Lapalme and Marie 
Loranger. TransType: Development-Evaluation 
Cycles to Boost Translator's Productivity. 
Machine Translation (Special Issue on 
Embedded MT Systems), vol. 17, num. 2, pp. 
77-98, Feb 2002. 
F.J. Och, R. Zens, H. Ney. Efficient Search for 
Interactive Statistical Machine Translation. 
Proceedings of the 10th Conference of the 
European Chapter of the Association for 
Computational Linguistics (EACL). Budapest, 
Hungary, pp. 387-393, April 2003. 
Antonio S. Valderrábanos, José Esteban and Luis 
Iraola. TransType2 - A New Paradigm for 
Translation Automation. MT Summit 2003, New 
Orleans, USA. 
