% *** I [ref] added a bit here

\mysubsection{Direct (non-IF-based) Translation Approaches}

We will experiment with a number of direct (non-IF) translation
approaches and will integrate them, along with the IF-based engines,
into a multi-engine system.  These will include (1) {\bf Example-Based
Translation}, using a generalized EBMT engine capable of producing
good results from a relatively small bilingual corpus.  It produces
direct translation between the languages \cite{brown:97}; and (2) {\bf
Glossary/Dictionary-based Direct Translation}, which tends to be of
lower quality than other techniques, but is simple enough that it is
possible to achieve very large vocabulary coverage, providing a
fall-back position when the other engines fail to produce a
translation.  This has proven useful in the
Pangloss \cite{pangloss94,pangloss95} and
DIPLOMAT \cite{frederking+al:97a,Frederking+et+al:99} projects.

% *** Ralf's research on this is NSF-funded, so it needs to be
% mentioned, as results of prior NSF support!

These direct approaches depend on the availability of bilingual data,
and we propose to initially limit experimentation therewith to the
translation between English and German. The direct translation
approaches will most likely not be fully integrated into the showcase
systems, given that the absence of the IF would make it more difficult
to account for multimodality in a principled way. We believe,
nevertheless, that extending the multi-engine approach to such
techniques will provide information concerning robustness, scalability
and domain portability which will prove valuable for future projects.
Our intention is to investigate the relative contribution of the
direct translation approaches to each of the above questions, as
compared with the IF-based approaches.

\mysubsection{Multi-engine Translation}

A significant amount of research effort in {\sc Nespole!} will be
given to combining the individual translation approaches in a
multi-engine system.  Such a paradigm combines several different
translation modules into a single architecture, with the goals of
capitalizing on their respective strengths, and minimizing their
weaknesses, in terms of robustness, scalability, domain portability
and translation quality.  

%% To this end, we plan to build upon the
%% results of projects such as Pangloss \cite{pangloss} and {\sc Diplomat}
%% \cite{diplomat}, as well as upon the technologies developed within
%% C-STAR and Verbmobil.

\begin{figure}[t]
\centerline{\psfig{figure=flow-chart-memt.60.ps,width=3.0in}}
%\epsfxsize=5.0in
%\centerline{\epsffile{flow-chart-memt.60.ps}}
\caption[Structure of MEMT architecture]{{\bf Structure of MEMT architecture}}
\label{memt-fig}
\end{figure}

As developed in the Pangloss and DIPLOMAT projects, the Multi-Engine
Machine Translation (MEMT) architecture \cite{Frederking+Nirenburg94}
(Figure~\ref{memt-fig}) feeds an input text to several MT engines in
parallel, with each engine employing a different MT
technology\footnote{Morphological analysis, part-of-speech tagging,
and possibly other text enhancements can be shared by the engines, if
available.}.  Each engine attempts to translate the entire input text,
segmenting each sentence in whatever manner is most appropriate for
its technology, and putting the resulting translated output segments
into a shared chart \cite{Kay67,Kay73,Winograd83} data structure after
assigning each segment a score indicating the engine's internal
assessment of the quality of the output segment.  These output ({\it
target language\/}) segments are indexed in the chart based on the
positions of the corresponding input ({\it source language\/})
segments.  Thus the chart contains multiple, possibly overlapping,
alternative translations.  We then use techniques based on statistical
language modelling (described in \cite{Brown+Frederking95}) to choose
the best overall set of outputs.

%% Since the scores produced by the engines are estimates of variable
%% accuracy, we use statistical language modelling techniques adapted
%% from speech recognition research to select the best overall set of
%% outputs \cite{Brown+Frederking95}.  These selection techniques attempt
%% to produce the best overall result, taking the probability of
%% transitions between segments into account as well as modifying the
%% quality scores of individual segments.  In essence, we do
%% Bayesian-style training to maximize the probability of a correct
%% translation given the available options, their estimated quality, and
%% the well-formedness of the output translation as determined by a
%% trigram language model.  Our MEMT techniques are an example of
%% integrating statistical and symbolic approaches; the MT engines that
%% we have employed to date have all been symbolic engines, while the
%% integration of their outputs is primarily statistical
%% \cite{Brown+Frederking95,Frederking94}.

The MEMT architecture makes it possible to exploit the differences between
different MT technologies.  Differences in translation quality, robustness,
scalability, and domain portability can all be exploited simply by merging the
best results from different engines.  When IF-based techniques can produce
high-quality, in-domain translations, their results will tend to be used; when
Example-based finds a high-quality match, those results will tend to be used.
When none of the other engines produces a high-quality result, the
Glossary/Dictionary engine supplies a lower-quality translation, which is
still much better than leaving the source language untranslated\footnote{As
implemented in Pangloss, unchosen alternative translations can be selected by
the user through a special-purpose interface \cite{Frederking+et+al93}, which
greatly increases the usefulness of the transfer-based translations.}.
The design of the MEMT architecture also allows easy integration of
new translation engines (such as direct statistical translation),
which fall beyond the scope of the work proposed under the current
project.

The main new research challenges in this area are:
(1) how best to extend the multi-engine framework to incorporate multiple 
IF-based and non-IF-based translation approaches; and (2)
the development of a significantly improved statistical framework for 
selecting an optimal combination result from the multiple engines.


