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\fancyhead[RO,RE]{137/06 Shuly Wintner}
\lhead{Part 3 - Detailed description of the research program}
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\section{Scientific background}
\subsection{Machine translation}
Translation is a difficult, delicate and fascinating art. From the early days of computers, it was conceived as one of the best candidates for demonstrating \textit{artificial intelligence}, and early systems for machine translation (MT) were among the first large-scale computer programs to be developed, leading to great excitement and hopes \cite{hutchins:97}. Some of the excitement subsided following the grim observations of \namecite{bar-hilel:60},  but the 1980's brought revival to this area of research, albeit with lower expectations. Even today, machine translation stands out as one of the most challenging applications of natural language processing.

Traditionally, two main approaches to machine translation have been considered. The first, known as the \textit{transfer} approach, follows the paradigm of analysis, transfer and generation. First, source texts are analyzed morphologically and syntacticly; then, syntactic structures are transferred to the target language; and finally, target texts are generated from the converted structures. The second approach, known as the \textit{interlingua} paradigm, takes analysis (and generation) further by converting source texts to internal, language-independent meaning representations, from which target texts can be generated. The interlingua approach is advantageous in multi-lingual translation scenarios, whereas the transfer approach is less costly since linguistic processing is more shallow.

More recently, however, machine translation has taken a shift towards more direct, statistically based approaches. In statistical machine translation \cite{brown:etal:90}, aligned parallel corpora are used to estimate the probability of a given phrase in the source language to be translated to a given text in the target language. Probabilistic models maximize the likelihood of one source sentence being translated to a target sentence. The main appeal of statistical machine translation is that is reduces the costly linguistic work to a minimum; the price to pay, however, is the need for large aligned corpora, which are only available for a small number of language pairs. In addition, the quality of statistical machine translation systems greatly depends on the domain of the texts: when trained on one domain, such systems usually perform poorly if tested on another domain.


\subsection{Hebrew processing}
Computational processing of Hebrew is complicated by the high level of
ambiguity posed by the language \cite{shuly:air03}. Standard Hebrew
orthography\footnote{To facilitate readability we use a
transliteration of Hebrew using ASCII characters in this document:\\
%\begin{center}
\begin{tabular}{llllllllllllllllllllll}
'&b&g&d&h&w&z&x&v&i&k&l&m&n&s&y&p&c&q&r&\$&t\\
<'>&<b>&<g>&<d>&<h>&<w>&<z>&<_h>&<_t>&<y>&<kN>&<l>&<mN>&<nN>&<s>&<`>&<pN>&<.sN>&<q>&<r>&<S>&<t>
\end{tabular}%{\end{center}%
} leaves most of the vowels unspecified. It does
not explicate \textnl{[a]} and \textnl{[e]} vowels, does not distinguish between
\textnl{[o]} and \textnl{[u]} vowels and leaves many of the \textnl{[i]} vowels
unspecified. Furthermore, the single letter \textnl{w} is used both for the
vowels \textnl{[o]} and \textnl{[u]} and for the consonant \textnl{[v]}, whereas
\textnl{i} is similarly used both for the vowels \textnl{[i]} and for the
consonant \textnl{[y]}. On top of that, the script dictates that many
particles, including four of the most frequent prepositions (\textnl{b} \textgl{in}, \textnl{k} \textgl{as},
\textnl{l} \textgl{to} and \textnl{m} \textgl{from}), the definite
article \textnl{h} \textgl{the}, the coordinating conjunction \textnl{w} \textgl{and} and some subordinating conjunctions (such as \textnl{\$} \textgl{that} and \textnl{k\$}
\textgl{when}), all
attach to the words which immediately follow them. Thus, a form such as
\textnl{mhgr} can be read as a lexeme (``immigrant''), as \textnl{m-hgr}
``from Hagar''or even as \textnl{m-h-gr} ``from the foreigner''. 
%Note that
%there is no deterministic way to tell whether the first \textnl{m} of the
%form is part of the pattern, the root or a prefixing particle (the
%preposition \textnl{m} ``from'').

An added complexity stems from the fact that there exist two main
standards for the Hebrew script: one in which vocalization diacritics,
known as \textnl{niqqud} \textgl{dots}, decorate the words, and
another in which the dots are missing, and other characters represent
some, but not all of the vowels. Most of the texts in  Hebrew
are of the latter kind; unfortunately, different authors use different
conventions for the undotted script. Thus, the same word can be
written in more than one way, sometimes even within the same
document. This fact
adds significantly to the degree of ambiguity, and requires creative solutions
for practical Hebrew language processing applications.

The challenge involved in constructing an MT system for Hebrew is amplified by
the poverty of existing resources. The collection of corpora for Hebrew is
still in early stages and all existing significant corpora are monolingual.
Hence the use of aligned bilingual corpora for MT purposes is currently not a
viable option.  There is no available large Hebrew language model which could
help in disambiguation. No publicly available bilingual
dictionaries currently exist, and no grammar is available from which transfer
rules can be extracted.


\section{Research objectives and expected significance}

We propose to develop a preliminary Hebrew-to-English Machine Translation
system under a transfer-based framework specifically designed for rapid MT
prototyping for languages with limited linguistic resources.  The task is
particularly challenging due to two main reasons: the high lexical and
morphological ambiguity of Hebrew and the dearth of available resources for
the language.  We will use existing, publicly available resources and adapt
them in novel ways to support the MT task.  The methodology behind the
approach is based on two separate modules: a transfer engine which produces a
lattice of possible translation segments, and a decoder which searches and
selects the most likely translation according to an English language model. We will also develop a manually crafted broad-coverage transfer
grammar and augment it with automatically acquired transfer rules.  Performance
will be evaluated on translation of Hebrew newspaper articles, using 
state-of-the-art measures for translation quality.  This system
will be the first large-scale Hebrew-to-English MT system ever to be
developed.  This development project will serve as an umbrella under which two separate yet related research programs will be carried out: machine learning based morphological disambiguation of Hebrew, and the development of a large scale, linguistically motivated grammar of Hebrew syntax.

The contribution of this research is manyfold. First and foremost, its main result will be the first large-scale machine translation system for Hebrew. Due to the limited commercial benefit of such a system (low body of users and huge development costs), such a project can only be carried out in academia. But the practical development work will drive basic and applicative research in several areas of linguistics and computational linguistics. We expect state of the art results in Hebrew morphological disambiguation, and the techniques that we will develop for this task will be equally applicable to Arabic and other Semitic languages. Furthermore, the development of the Hebrew grammar will contribute to better understanding of Hebrew syntax and its interfaces with morphology, semantics and pragmatics. Finally, a by-product of this work will be the creation, extension and improvement of several language resources, including monolingual and bilingual corpora, the Hebrew lexicon, a bilingual translation dictionary etc.

\section{Detailed description of the proposed research}
\subsection{Working hypothesis}
The system we propose to develop is based on a transfer-based framework
specifically designed for rapid MT prototyping for languages with limited
linguistic resources.  This framework has been under development for the past
few years by the MT research group at Carnegie Mellon, under the leadership of
Dr. Lavie and his colleagues \cite{amta-02,avenue-mt-journal,TALIP-SLE-03}.  While the framework itself is still research
work in progress, it is sufficiently well developed for serious
experimentation.  The framework includes a declarative formalism for symbolic
transfer grammars.  A grammar consists of a collection of transfer rules,
which specify how phrase structures in a source-language correspond and
transfer to phrase structures in a target language, and the constraints under
which these rules should apply.  The framework also includes a
fully-implemented transfer engine that applies the transfer grammar to a
source-language input sentence at runtime, and produces all possible word and
phrase-level translations according to the grammar.  This framework was
specifically designed to support advanced research on methods for
automatically acquiring transfer grammars from limited amounts of elicited
word-aligned data.  The framework also supports manual development of transfer
grammars by experts familiar with the two languages.


The current trend in machine translation is leaning towards
\textit{statistical MT}. Several research state-of-the-art MT systems for
Chinese-to-English and for Arabic-to-English have been developed under this
paradigm in the last few years.  While statistical MT can produce impressive
results in many cases, such approaches suffer from two limitations. First,
they crucially rely on the existence of a very large scale parallel corpus of
texts, consisting of aligned texts in the two languages. Such a corpus is
unavailable for Hebrew. Second, statistical MT systems are notoriously
dependent on the domain for which they were developed (and on which they are
trained). Testing such systems on text from different domains yields extremely
poor results. Transfer-based systems, on the contrary, are more robust to such
changes and are hence more broad-coverage.  Most existing state-of-the-art
commercial MT systems follow a transfer approach, but are predominantly purely
rule-based.  Achieving high-levels of coverage and accuracy in these systems
has typically required hundreds of person-years of manual labor.  These
labor-intensive transfer systems are therefore economically feasible for only
a small set of language pairs, and the approach is not particularly appealing
for Hebrew.  The innovative ideas behind the underlying framework of our
proposed system have the potential of achieving similar translation
performance, with orders-of-magnitude less human labor.

\subsection{Preliminary results}
We have already constructed a prototype end-to-end Hebrew-to-English system
under this framework, with limited funding granted by the Caesarea Rothschild Institute for Interdisciplinary Application of Computer Science at the University of Haifa \cite{shuly:tmi04}.  The performance of the system developed
so far is promising but leaves much room for improvement.  The general
architecture of the system is already in place and fully implemented.  The
extended system proposed here will follow the same design principles, which we
outline below.

The system consists of the following main components: a Hebrew input
sentence is pre-processed, and then sent to a \textit{morphological analyzer},
which produces all possible analyses for each input word, represented in the
form of a lattice of possible input word lexemes and their morphological
features.  The input lattice is then passed on to the \textit{transfer
engine}, which applies a collection of lexical and structural
\textit{transfer rules} in order to parse, transfer and generate English
translations for all possible word and phrase segments of the input.  This
comprehensive collection of output segments is stored in an output lattice
data-structure.  The lexical transfer rules used by the transfer engine are
derived from a \textit{bilingual dictionary}, while the higher-level
structural transfer rules come from either a manually-developed or
automatically-acquired \textit{transfer grammar}.  In the final stage, the
English lattice is fed into a \textit{decoder} which uses a \textit{language
model} of English to search and select a combination of sequential translation
segments that together represent the most likely translation of the entire
input sentence.  A schematic diagram of the system architecture can be seen in
Figure~\ref{sys-diagram}. We briefly describe below the main components of the system.

\begin{figure*}[t]
\begin{center}
\includegraphics[scale=.5,angle=270]{Hebrew-sys-diagram.eps}
\end{center}
\caption{Architecture of the Hebrew-to-English Transfer-based MT System and its Major Components}
\label{sys-diagram}
\end{figure*}

Morphological processing of the Hebrew input is based on HAMSAH \cite{shuly:shlomo:acl05}, a finite-state morphological grammar of Hebrew which drives a large coverage and efficient morphological analyzer. The grammar is based on a lexicon of over 20,000 entries, which is presently being maintained by the Knowledge Center for Processing Hebrew \cite{shuly:shlomo:03}.
%
To emphasize the richness of the information obtained by morphological analysis of Hebrew, consider the following example, which is obtained for the form \textnl{b\$wrh} ``in the line'':
\begin{verbatim}
[+preposition]b[+definiteArticle][+noun][+id]7081[+undotted]$wrh
  [+gender]+F[+number]+singular[+script]+formal[+construct]+false
\end{verbatim}
The analysis provides information on two prefix particles, an overt preposition (\textnl{b}, ``in'') and a covert definite article. The main part of speech of the word is \textit{noun}, and the \textit{id} provides a link to the lexicon. The \textit{undotted} field is a representation of the lexeme. Information on number, gender, and status (construct/absolute) follows, along with an indication of adherence to the standard script. As another example, consider the verb \textnl{\$bth}:
\begin{verbatim}
[+verb][+id]10433[+undotted]$bt[+root]$bt[+binyan]+Pa'al
  [+person/gender/number]+3p/F/Sg[+script]+formal[+tense]+past
\end{verbatim}
Here, information is provided on the root, pattern and tense of the verb, in addition to person, number, and gender.

Finally, the current system is based on a very limited bilingual dictionary which was automatically acquired from a digital version of a low-coverage, low-quality printed dictionary \cite{dahan:97}. The dictionary is integrated with the Hebrew lexicon, but only a small fraction of the Hebrew entries have English translations.
%
As for transfer rules, the current system incorporates a small number (below~30) of manually crafted rules, mostly accounting for noun phrases.

Once the system was reasonably stable, we
devoted about two weeks of time to improving it based on a small
development set of data.  For development we used a set of 113 sentences from
the Hebrew daily \textit{HaAretz}.  Average sentence length was approximately
15 words.  Development consisted primarily of fixing incorrect mappings before
and after morphological processing and modifications to the bilingual lexicon.
The small transfer grammar was also developed during this period. Given the
limited resources and the limited development time, we find the results to be
highly encouraging.  For many of the development input sentences, translations
are reasonably comprehensible. Figure~\ref{trans-exmpls} presents a few select
translation examples from the development data.

\begin{figure*}[hbt]
\begin{center}
\begin{verbatim}
maxwell anurpung comes from ghana for israel four years ago and since 
worked in cleaning in hotels in eilat 

a few weeks ago announced if management club hotel that for him to 
leave israel according to the government instructions and immigration 
police 

in a letter in broken english which spread among the foreign workers 
thanks to them hotel for their hard work and announced that will 
purchase for hm flight tickets for their countries from their money 
\end{verbatim}
\end{center}
\caption{Select Translated Sentences from the Development Data}
\label{trans-exmpls}
\end{figure*}


\subsection{Experimental design and methods}
While the main, practical outcome of the proposed research is a fully working Hebrew to English machine translation system, this project will also serve as an umbrella under which two separate yet related research programs will be carried out: machine learning based morphological disambiguation of Hebrew, and the development of a large scale, linguistically motivated grammar of Hebrew syntax. This section details the two main research programs, as well as two other components which we will develop.

\subsubsection{Morphological processing}
Morphological analysis will be performed using HAMSAH, as in the current system. However, the performance of this component will be significantly improved by introducing state of the art, machine learning based morphological disambiguation.


The current system uses \textit{all} the possible analyses produced by HAMSAH, applying no contextual cues to disambiguate them. Morphological disambiguation of Hebrew is a much more challenging task than English (and other European languages) part-of-speech (POS) tagging due to the following factors:

\begin{description}
\item[Segmentation] A single token in Hebrew can actually be a sequence of more than one lexical item. Consider the token \textnl{b\$wrh}, whose analyses are listed in figure~\ref{fig:bswrh}.
\begin{figure*}
\begin{verbatim}
[+noun][+id]10655[+undotted]b$wrh
  [+gender]+F[+number]+singular[+script]+formal[+construct]+false
[+preposition]b[+noun][+id]7081[+undotted]$wrh
  [+gender]+F[+number]+singular[+script]+formal[+construct]+false
[+preposition]b[+definiteArticle][+noun][+id]7081[+undotted]$wrh
  [+gender]+F[+number]+singular[+script]+formal[+construct]+false
[+preposition]b[+noun][+id]12612[+undotted]$wr
  [+gender]+M[+number]+singular[+script]+formal[+construct]+false
  [+possessiveSuffix]+3p/F/Sg
\end{verbatim}
\caption{The analyses of the token \textnl{b\$wrh}}
\label{fig:bswrh}
\end{figure*}
Observe that the lexical item in the first analysis is \textnl{b\$wrh} \textgl{gospel}, the second and third analyses have \textnl{\$wrh} \textgl{line} as their lexeme whereas the base of the last analysis is \textnl{\$wr} \textgl{bull}. Consequently, the POS tags of the four analyses are \textit{N, P+N, P+Det+N} and \textit{P+N+Poss}, respectively.
%\begin{center}
%\begin{tabular}{|c|c|c|c|}
%\hline
%0 & 1 & 2 & 3  \\ \hline\hline
%\multicolumn{4}{|c|}{\textnl{b\$wrh}}\\ \hline
%\textnl{b} & \multicolumn{3}{c|}{\textnl{\$wrh}}\\ \hline
%\textnl{b} & \textnl{h} & \multicolumn{2}{c|}{\textnl{\$wrh}}\\ \hline
%\textnl{b} & \multicolumn{2}{c|}{\textnl{\$wr}} & \textnl{h}\\ \hline
%\end{tabular}
%\end{center}
\item[Large tagset]
In contrast to the limited tagset of standard POS tagging approaches in English, the number of different tags in a language such as Hebrew (where the POS, morphological features and prefix and suffix particles are considered) is huge. For example, the Hebrew morphological analyzer that we will be using in this work produces 12 different parts of speech, some with subcategories; 6 values for the number feature (including disjunctions of values), 4 for gender, 5 for person, 7 for tense and 3 for nominal status. Possessive pronominal suffixes can have 15 different values, and prefix particle sequences can theoretically have hundreds of different forms, although in practice only a few dozens are witnessed in a standard corpus. While not all the combinations of these values are possible, we estimate the number of possible analyses to be in the thousands.
\item[Ambiguity] Due to the rich morphology and the problems of the orthography, Hebrew is highly ambiguous. In a particular corpus of 40,000 word tokens, the average number of analyses per word token was found to be 2.1, while 55\% of the tokens were ambiguous~\cite[p.~385]{levinger:95}. Furthermore,
in many cases two or more alternative analyses share the same part of speech, and in extreme cases two or more analyses are completely identical, except for their lexeme, as in \textnl{xlw} ``happened'' or ``became ill'' or \textnl{\$bth} ``stroke'' or ``captured'' (figure~\ref{fig:verbs}).
\begin{figure*}
\begin{verbatim}
[+verb][+id]6967[+undotted]xl[+root]xwl[+binyan]+Pa'al
  [+person/gender/number]+3p/M/Pl[+script]+formal[+tense]+past
[+verb][+id]9486[+undotted]xlh[+root]xlh[+binyan]+Pa'al
  [+person/gender/number]+3p/M/Pl[+script]+formal[+tense]+past
  
[+verb][+id]10433[+undotted]$bt[+root]$bt[+binyan]+Pa'al
  [+person/gender/number]+3p/F/Sg[+script]+formal[+tense]+past
[+verb][+id]11664[+undotted]$bh[+root]$bh[+binyan]+Pa'al
  [+person/gender/number]+3p/F/Sg[+script]+formal[+tense]+past
\end{verbatim}
\caption{Verbs with identical analyses, up to the lexeme}
\label{fig:verbs}
\end{figure*}
\item[Anchors] which are often function words, are almost always morphologically ambiguous in Hebrew. For example, the most frequent token in Hebrew is \textnl{at} which is almost always a preposition (the accusative marker), but sometimes a personal pronoun (second person feminine singular) and, very rarely, a noun (\textgl{spade}). Similarly, the frequent genitive preposition \textnl{\$l} \textgl{of}, in some of its inflections, is highly ambiguous. Many of the function words which help boost the performance of English POS tagging are actually prefix particles which add to the ambiguity in Hebrew (e.g., the definite article \textnl{h} \textgl{the} or the coordinating conjunction \textnl{w} \textgl{and}).
\item[Word order] in Hebrew is relatively free, and in any case much freer than in English.
\end{description}


We have conducted a small experiment with POS tagging for Hebrew. Taking advantage of the Hebrew treebank data we selected a corpus of 35811 word tokens, analyzed it automatically using the analyzer of \namecite{segal:97} and considered only the POS data. The size of the tagset is~12, and the distribution of tags in the corpus is as in table~\ref{tbl:posdata}.
The average number of analyses per word is~2.18, distributed according to the histogram of table~\ref{tbl:hist}. The baseline, obtained by picking the most frequent POS tag per word, is 78.26\%. 
%Interestingly, a na\"{\i}ve HMM results in 77.61\% accuracy, slightly below baseline, probably due  to data sparseness. 

\begin{table*}
\begin{center}
\begin{tabular}{lr@{\hskip 1cm}lr}
Tag & \%&Tag & \%\\
Noun & 30.1 & Adverb & 4.1\\
Conjunction & 17.5 & Pronoun&4.0\\
Verb & 13.6 & Numeral& 3.0\\
Preposition & 10.0 & Particle  &2.0\\
Adjective& 7.5 & Auxiliary&0.7\\
Proper Noun &6.9 & Interrogative&0.2\\
\end{tabular}
\caption{POS frequencies in a small Hebrew corpus}
\label{tbl:posdata}
\end{center}
\end{table*}

\begin{table*}
\begin{center}
\begin{tabular}{lrrrrrrrrrrrr}
\# analyses & 1&2&3&4&5&6&7&8&9&10&11&12\\
\# words & 17106&7753&4619&3272&1512&648&388&307&128&45&23&10
\end{tabular}
\caption{Number of analyses per word in a small Hebrew corpus}
\label{tbl:hist}
\end{center}
\end{table*}

We will apply a machine learning approach to morphological disambiguation involving a large number of targets, where a structure can clearly be imposed on the tags. Given the large number of potential targets, we address the
problem as one of combining several classifiers, each predicting the value of
one of components of the analysis. The results of the na\"{\i}ve classifiers will be combined in a sophisticated manner, taking into account the constraints that hold among the various components.

Making natural language decisions often involves assigning values
to sets of variables where complex and expressive dependencies can
influence, or even dictate, what assignments are possible.
Efficient solutions have been given to problems of this kind under
some restrictions. One class of solutions decouples the process of
learning estimators to variables values from the
inference process that is applied later, to derive a global
assignment to the set of variables of interest. A second class of
solutions incorporates the dependencies among the variables into
the learning process, and directly induces estimators that yield a
(good) global assignment. HMMs, conditional models
~\cite{punyakanok-roth:01,McCallumFrPe00} and a large number of
dynamic programming schemes used in the context of
sequential predictions (e.g., Named
Entities~\cite{TjongKimSangDe03}) fall into the first category.
Conditional Random Fields \cite{CRF} and other discriminative
learning methods with Markov assumptions are in the second.

We have already successfully applied the idea of linguistically motivated combination of na\"{\i}ve classifiers to the problem of identifying the root of Hebrew and Arabic words \cite{shuly:emnlp04,shuly:ezra:2005}. We plan to utilize the same techniques, as well as novel ones which will be developed in the course of the proposed research, to the problem of morphological disambiguation.



\subsubsection{Transfer rules}
Transfer rules are what makes our system superior to statistical machine translation, in light of the relatively limited resources at our disposal. We will continue to improve the quality of the translations by developing a large scale, linguistically accurate grammar of Hebrew, from which a transfer grammar will be generated. 

Currently, there exists no wide-covergae computational grammar of Hebrew. The most extensive attempt so far is probably the grammar of \namecite{shuly:jnle}, which is already outdated and very limited in its coverage. We will use a modern formalism, most probably HPSG \cite{hpsg2,sag:wasow:99} for this part of the project. The grammar will be an extension of the fragments developed by \namecite{shuly:acl-98}, \namecite{shuly:jl} and \namecite{Melnik_CG04}, and will be fully implemented. Furthermore, it will be integrated with the computational lexicon and morphological analyzer mentioned above, ensuring wide coverage. The grammar will be developed, tested and evaluated using the corpora that are collected at the Knowledge Center for Processing Hebrew.

This grammar will be used to induce a transfer grammar for the benefit of the machine translation system. Transfer rules in this paradigm are unification-enhanced context-free rules, so an adaptation of the linguistically motivated, deep analysis grammar to the required format should be a relatively easy task. This phase of the proposed research has the additional benefit of contributing to the theoretical investigation of Hebrew syntax and its interfaces with morphology, semantics and pragmatics.



\subsubsection{Bilingual translation}
The current system uses a very limited and low-quality bilingual dictionary. We will improve the quality of this module by incorporating both manually crafted and corpus induced translation dictionaries.

The core of the work in this module of the project is the tedious, incremental effort of lexicographers. We already have developed a graphical user interface for editing the lexicon; as the bilingual dictionary is now fully integrated with the lexicon, the task of adding word translations to the dictionary is eased.
%
In addition to the manual extension of the coverage of the dictionary, we will investigate automated methods for inducing pairs of word translations. However, due to the limited availability of parallel Hebrew-English corpora, we do not expect this path of research to contribute significantly to the end result.


\subsubsection{English to Hebrew translation}
The core of this proposal deals with Hebrew to English machine translation only. However, it should be possible, with relatively limited additional effort, to produce also an English to Hebrew translation system. We will investigate this possibility when the Hebrew to English part of the system is mature.

The components that are required for the reverse direction are as follows:
\begin{itemize}
\item
English tokenization and morphological analysis. This task is significantly easier for English than it is for Hebrew; several tools are freely available.
\item English-Hebrew word translation. We will use the inverse of our Hebrew-English dictionary; while this is a sub-optimal solution, it should provide reasonable quality for this application.
\item
Transfer rules. As the formalism we work in is declarative, it should be possible to convert the Hebrew-English transfer rules to their English-Hebrew counterparts with minimal effort.
\item Hebrew morphological generation. Since HAMSAH is developed using finite-state technology, it is fully reversible and can be used for generation just as well as for parsing.
\item Hebrew language model. While such a resource is currently unavailable, it should be easy to create using the corpora of the Knowledge Center.
\end{itemize}
We will spend moderate effort on this direction of the translation system, mostly as a proof-of-concept prototype system.


\subsection{Available resources}
The Department of Computer Science at the University of Haifa provides excellent research facilities to faculty and graduate students alike. Both the PI and the research assistants will have office space and access to adequate computing equipment. We will also need corpora for training and annotated corpora for evaluation. Fortunately, a corpus of Hebrew is currently being collected at the Knowledge Center for Processing Hebrew \cite{shuly:shlomo:03} and will be made available for our project. The corpus is currently being annotated by the Knowledge Center. A state-of-the-art morphological analyzer for Hebrew is currently being developed by the PI and will be ready for the beginning of this project. The transfer engine and an English language model will be provided for this project by Alon Lavie's group at CMU (see attached letter of cooperation).


\newpage
%\section{Bibliography on research area} 
\bibliographystyle{fullname}
\bibliography{all,mt}

\newpage
\section*{Time schedule and work plan}
\begin{center}
\begin{tabular}{|l|r|r|}\hline
 & \multicolumn{1}{c|}{Beginning}
 & \multicolumn{1}{c|}{End}\\
 & \multicolumn{1}{c|}{Month/Year}
 & \multicolumn{1}{c|}{Month/Year}\\ \hline
Prepare resources (annotation, lexicon extension) & 10/06 & 9/07\\ \hline
Extend bilingual dictionary & 10/07 & 9/08 \\ \hline
Develop morphological disambiguation module & 10/07 & 9/09\\ \hline
Develop Hebrew grammar & 10/06 & 9/08 \\ \hline
Adapt grammar for transfer & 10/08 & 9/09\\ \hline
Integration, demonstration, dissemination of results & 3/09 & 9/09\\ \hline
\end{tabular}
\end{center}


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