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\begin{center}
{\bf Suggestions for Collaboration between {\sc Avenue} and {\sc
Catanal}}\\
\begin{small}
Lori Levin, Alon Lavie, Jaime Carbonell, Alex Waibel, Ralf Brown, John 
Lafferty\\
Language Technologies Institute\\
Carnegie Mellon University\\
lsl@cs.cmu.edu
\end{small}
\end{center}

\section{Introduction}

Machine Translation (MT) systems have been built for a variety of
language pairs.  A quick glance at the Compendium of MT Software
suggests that English and Japanese are the most popular languages for
commercial MT systems.  Major European languages come next, followed
by a few minor European languages (e.g., Basque and Slovenian), and
some Middle Eastern languages.  Finally the compendium shows less than
a handful of MT systems for minor indigenous languages in Africa,
North America, and the former Soviet Union.  The reason for the bias
toward the languages of major trade partners is largely economic:  MT
systems are expensive to develop.  Some commercial systems represent a 
person-century of work.

The absence of MT research on indigenous languages is a missed
opportunity socially and a tragedy scientifically.  From the scientific 
point of view MT systems are being developed without taking into
account all types of languages.  For example, we do not know of an MT
system for a polysynthetic or incorporating language, and we do not
know whether all current MT techniques are suitable for those types of 
languages.  This is a flaw in research that is supposed to be
applicable to all languages.  The missed social opportunity is for
speakers of indigenous languages to participate in government,
education, health care, and the internet without having to give up
their languages.  

This white paper sketches some plans for collaboration of the {\sc
Catanal} project with Carnegie Mellon's {\sc Avenue} project.  The
suggested role of {\sc Avenue} is to provide a machine translation
program, whereas the suggested role of {\sc Catanal} is to assist the
native community in finding uses for MT in education and language
preservation, to design front end applications using MT as a back
end, and to install MT-based technology in the communities.

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First we will give a brief history of {\sc Avenue} (which was
originally called {\sc Nice}) then describe some basic issues in MT
design, and finally describe potential research directions.

\section{History of the NICE/AVENUE Project}

The {\sc Nice} (Native language interpretation and communication
environment) project was originally created Gary Strong (NSF) in
response to a series of NSF/OAS workshops on Western Hemisphere
collaboration.  Machine Translation was proposed as a step toward
digital government, closing the digital divide, and better
communication between governments and citizens.  However, in order to
close the digital divide, MT needed to be made available to people who
speak neither Spanish nor English.  Jaime Carbonell, Director of the
Language Technologies Institute, attended some of the workshops.
Since LTI had already been doing research on MT for minor languages,
the {\sc Nice} project was created and funded at LTI.  The original
funding came from DARPA during the time that Gary Strong was a program
manager there.

Prior to the {\sc Nice} project, Peter Wilkniss had organized a
workshop for {\sc Catanal}, Computer Aided Translation of Alaska
Native Languages.  Lori Levin attended that workshop, and used it as a 
model for {\sc Nice}, the most important points being that the project
is a partnership of scientists and native people, and that the
native people design the project for the needs of their communities.  

{\sc Nice} then formed a partnership with the Institute for Indigenous
Studies at the Universidad de la Frontera, in Temuco, Chile.  This
partnership has been in place for about 1.5 years.  The team in Chile
is recording, transcribing, and translating data in Mapudungun (the
language of the Mapuche people).  The data will be used as training
data for MT research.  So far 80 hours of speech have been recorded
and transcribed, and 200K words of paper text have been typed into
electronic form.  The choice of data and issues of orthography in the
control of the team in Chile.  The data is recorded and transcribed in
Chile under the supervision of Mapuche linguists and is freely
available to the Mapuche community for whatever uses they choose.

LTI researchers would also like to pursue a partnership with {\sc
Catanal} and the I\~{n}upiat community in Barrow, Alaska.  In addition 
the original {\sc Catanal} workshop, two meetings have taken place.
A brief meeting at the Alaska Native Educators Conference in
Anchorage and a two-day meeting in Barrow.

Recently, because of changes in program managers, the work of the {\sc
Nice} project has been taken over by two new projects at the LTI: A
DARPA project called MilliRADD (Rapidly Adaptable Data Driven MT for
small amounts of training data), and an NSF ITR Project called {\sc
Avenue}.  Research on indigenous languages is continuing under {\sc
Avenue}.  MilliRADD is focusing on Chinese and Arabic so as to have a
common ground for evaluation in comparison to other DARPA funded MT
projects.

\section{Some Basic Issues in Machine Translation}

An ideal MT system would produce high quality translations of material
in every style of writing or speaking on every topic, and would
require only a few weeks to adapt to a new language.  In actual
practice, each MT system achieves some, but not all of these goals.
For example, one MT system produces high quality translations of
technical manuals, but requires the input language to be closely
constrained, and takes years to develop.  This is useful for an
industry that needs to disseminate clear technical material to its
customers.  Another MT system can be adapted cheaply to new languages
in a matter of weeks, but provides a rough translation that is just
good enough to give a gist of the meaning to an intelligence analyst.
Spoken language translation systems for face-to-face conversation can
handle disfluent spontaneously produced speech, but are limited to
specific topics (like travel arrangements), and produce medium quality
translations that can be negotiated and repaired by the participants
in the conversation.  

In short, there are many types of MT, and each MT project has to be
designed to match what it will be used for.  For a collaboration
between {\sc Catanal} and {\sc Avenue}, the primary uses might be
translation of educational material (e.g., science, math, and history)
into I\~{n}upiaq for use in immersion classes, or development of
lessons for teaching the I\~{n}upiaq language itself.  In the
following sections, we review the pros and cons of different types of
MT for the Northern Alaska setting.

\section{Human Engineered vs Automatically Learned MT}

\subsection{Human Engineered MT}

Human Engineered MT is a tried and true method.  It produces the
highest quality output.  To produce a human-engineered system,
linguists and computational linguists write rules for the source and
target languages.  Following are the pros and cons:

\begin{itemize}
\item {\bf Advantages of Human Engineered MT}
\begin{itemize}
\item High quality output:  This is important for a community that
wants to really use MT, and not just participate in risky research.

\item Can start right away:  Human linguists can start writing rules
without waiting for lengthy data collection procedures.

\item Human-readable rules:  The system consists of coded grammar
rules  that can be read and modified by humans.
\end{itemize}

\item {\bf Disadvantages of Human Engineered MT}
\begin{itemize}
\item Long development time:  In the end, the MT system produces high
quality output, but it may take up to two years for there to be 
broad coverage.

\item Known method/Not new science:  Work on human-engineered MT would 
probably not be funded by NSF's CISE or linguistics programs.

\item Extensive human expertise required:  Building a human engineered 
MT system requires humans that are fluent in both languages and who
are trained in computational linguistics.
\end{itemize}

\end{itemize}

\subsection{Automatically Learned MT}

There are many methods for developing MT systems without human
linguists.  These methods consist of computer programs that learn
translation correspondences from a corpus of previously translated
texts.  

\begin{itemize}
\item {\bf Advantages of Automatically Learned MT:}
\begin{itemize}
\item Participate in cutting edge research:  Automatic methods have
been designed by computer scientists who are not familiar with
language typology and the range of differences between languages.
Speakers of polysynthetic languages can participate in this research
and ensure that the methods that are developed are applicable to all
language types.

\item Can be built quickly (after data collection):  After a large
enough database has been collected (see below), the first version of
the MT system can be running in a matter of weeks.

\item Division of Labor: A computer scientist can build the MT system
by feeding it previously translated texts without knowing anything
about the language.  Native speakers provide the texts and do not have 
to know computer science or computational linguistics.

\item Coverage:  An automatically learned system can produce some kind 
of output for any input.  A human-engineered system only covers
material that the humans have had time to cover. 
\end{itemize}

\item {\bf Disadvantages of Automatically Learned MT:}
\begin{itemize}
\item Data requirements:  Algorithms for automatically learning
translation correspondences must be trained on corpora that are over
one million words long.  Some methods require ten million words.  The
reason is that the program needs to see many different ways of saying
the same thing in many different contexts and also needs a
statistically significant number of examples to work with.  If such a
corpus doesn't already exist, it can take more than six months to
create it by gathering and typing material.  Current research has made
some progress on reducing the data requirements.

\item Quality:  The state of the art in automatically learned MT is
that the quality is not very high.  DARPA finds it useful because it
may be just readable enough to give a gist of the meaning, and then
the document can be handed off to a language expert for more careful
translation.  Maybe it would be useful for web browsing.

\item Not human-readable: The translation ``rules'' of an
automatically learned MT program are actually a collection of
statistics that cannot be understood by humans.  Of course, a human
can change the ``rules'' system by changing the learning algorithm and
the learning data, and then letting the system learn again.
\end{itemize}

\end{itemize}

\subsection{Hybrid Systems}

A variety of methods are available for mixing human-engineered and
rule-based systems.  One method is multi-engine MT.  Under this
method, a human-engineered and an automatically-learned system both 
translate the same material, and their outputs are combined
statistically in a way that uses the best from both methods.

\section{Recommendations and Estimates of Effort}

\begin{itemize}
\item Carnegie Mellon is already investigating several methods for
automatically learned MT.  We would be very happy to have the
I\~{n}upiat community participate with us on this research, especially 
since we would like to test our methods on polysynthetic languages.
Carnegie Mellon can do this work under its current {\sc Avenue}
funding.  

\item In order for the I\'{n}upiat community to participate in the
research on automatically learned methods, the community would have to
gather up its lexicons/dictionaries and texts.  This might be up to
six months work.  {\sc Avenue} could supply some seed funding to get
it started, but this data collection effort would then require more
funding -- maybe two people for six months with some supervision by
knowledgeable community leaders.  Collection of spoken data for speech
recognition would take an additional few months. The data collection
effort would be of general use to the I\~{n}upiat community (e.g.,
reviewing elders' conferences) beyond its use for machine translation.

\item There should be a human engineered component to the project.
If the quality of automatically learned methods is not adequate,
I\~{n}upiat people may feel that their time and money has been wasted.
They need an MT system, not just risky research.  However, the
human-engineered component cannot be funded by CISE because it is a
known technique and not new research.  This work would need a
significant amount of funding: at least one full time I\~{n}upiat
speaker for two years, part time supervision by community leaders with
knowledge of linguistics, one graduate student at Carnegie Mellon for
two years, and faculty supervision at Carnegie Mellon.

\item Multi-engine integration of human-engineered and automatically
learned systems.  Carnegie Mellon can do this under {\sc Avenue}
funding.  

\item Front-end application:  After a machine translation problem is
like a bare engine for a vehicle.  The rest of the vehicle needs to be
designed and deployed: for example, who will use it and for what
purpose, what kind of user interface will it have, if it will be used
in education, which part of the curriculum will it fit into and what
kind of educational lesson plans go with it.  The {\sc Catanal}
project and the I\~{n}upiat community will carry out this part of the
work, which will also require funding.
\end{itemize}



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