While some of the issues we encounter are specific to our project,
others are common to all systems that automatically elicit a
controlled corpus.  Other work in this area includes Nirenburg et
al. \cite{Nirenburg1998,NirenburgRaskin1998,SherematyevaNirenburg2000},
who describe the construction of a controlled corpus based on
linguistic research across languages.  They compiled a list of
linguistic features together with their possible values across
languages.  A bilingual speaker then provides information, namely what
values a certain feature can take in the language in question.  The
elicited information is then used to build a machine translation
system between the elicitation language and the elicited
language. This work ties in well with our project as it relies on a
bilingual speaker to compile a knowledge base for the system.
	
\cite{JonesHavrilla1998} also use an expert user who translates
well-chosen sentences and annotates the translations with glosses.
The glosses are used to grow a knowledge base for a machine
translation system. In order to build a machine translation system for
a new language pair, expert users collect a set of representative
sentences from such sources as grammar books.

In the \textsc{Avenue} project we also draw from such sources as well
as from guides for field linguists
\cite{ComrieSmith1977,BouquiauxThomas1992}.  In essence, our
elicitation corpus emulates the work of fiel linguists, eliciting
sentences that cover different linguistic phenomena, and pursuing
different directions based on previous discoveries.

For each bilingual sentence pair, one or more (if remaining
ambiguities are present) transfer rules are produced and immediately
validated: the transfer engine uses the newly created rule to
translate the source language sentence of the pair it was produced
from.  A transfer rule hypothesis is only retained for further
processing if it correctly produces the target sentence.

%% We have to say why this is harder than it sounds.  

We are currently investigating the
possibility of retaining suboptimal transfer rules, i.e. rules that
cannot be used to translate the sentence that they were derived from,
and apply recovery and refinement techniques to them.  



%% alternative abstract (from proposal draft)

This document describes a thesis that aims at automatically inferring syntactic transfer rules for Machine Translation from bilingual, word-aligned text.  Unlike other approaches, the present system can obtain the bilingual data via structured elicitation: a bilingual user is asked to translate a set of sentences that will maximize the linguistic coverage of the system given the amount of training data.  The thesis will also explore ways for the system to infer transfer rules from unstructured text. In either case, syntactic transfer rules are learned from this bilingual corpus in three stages: the in the first stage, "seed rules"  with no syntactic structure are produced, rules that are general enough to scale to some unseen examples, but that retain enough information to be mostly specific to the sentence pairs that they were derived from. Subsequently, syntactic structure is added by using previously learned rules for simpler constituents.  In the third stage, the seed rules are generalized in order to make them applicable to more unseen examples. If such generalization fails, the seed rules provide a robust fallback.  Generalization is done using a Seeded Version Space Learning algorithm.  Unlike traditional version space learning, this algorithm starts from an informed guess at the desired hypothesis, and works from this guess.  Thus, Seeded Version Space learning does not suffer from worst-case exponential complexity - rather, the algorithm is polynomial in the input size.  Starting from an informed guess also results in faster convergence.
At all times during learning, each proposed transfer rule is immediately validated by actual translation using a transfer engine.  If the translation power of a rule does not meet specific requirements, it is not accepted.
The system works under the assumption that natural language processing (NLP) tools such as a grammar, a lexicon, etc., are available for one of the languages in the translation pair, the so-called resource-rich language. For the other language, no such assumption is made (resource-poor language).  However, any information about the target language is naturally encoded into the learning process. 
The scientific contribution of this work is twofold: first, syntactic transfer rules are learned from a bilingual corpus.  This differs from the traditional transfer-based systems in that the rules no longer take many person-years to develop.  It also differs from common data-driven methods (such as example-based or statistical machine translation) that derive more shallow models such as statistical word associations. Second, those transfer rules are refined and scaled up to cover more unseen examples with a Seeded Version Space Learning algorithm. This method represents a novel way of viewing transfer rules as residing in a version space that can be explored via immediately validated generalization. A prototypical version has been implemented, and an initial study has shown that the system can successfully infer transfer rules for English-German translation.  The translation power of the learned rules has been evaluated by cross-validation with promising results.







\textit{Note: where does the following go??\/}
The rules that our system learns can be used as a stand-alone translation grammar (where the grammar is possibly improved by hand or by the translation correction tool).  In addition, the grammar can also be integrated with other MT systems, such as an EBMT system, to function in a multi-engine system. Both of these points will become clearer in the course of the discussion.

%%This should stay in
Most corpus-based MT methods require large bilingual training
corpora.  The corpora must be large because the programs are learning
statistical distributions, and not linguistic generalizations. 
Statistical methods could use corpora on the order of ten million
words in length.  Recent experiments with one hundred thousand word
training corpora \cite{TIDES} have shown that statistical methods may
be possible with smaller corpora, but results are considerably worse
than when larger corpora are used.  
%% Is this a sound comparision between our approach and SMT?


%%This as well
Our elicitation approach does not require the existence of large
bilingual training corpora.  The elicitation corpus is a monolingual
corpus in the resource-rich language.  (We currently have elicitation
corpora in English and Spanish.)  The corpus is organized
linguistically with small phrases building up compositionally into
larger phrases, and sentences designed to detect various kinds of
inflections, agreements, and constraints in the resource-poor
language.  Ideally, all that would be required from the resource-poor
language is a person who is literate in both the resource-rich and
resource-poor languages.  (Section~\ref{experiment} describes the
current state of our research, which requires a literate bilingual and
a lexicon with inflectional features in the resource-poor language.)
By using carefully chosen phrases that cover basic sentence structures
and syntactic constraints our system learns rules from less noisy data
and we avoid the need for a corpus that is large enough to calculate
statistical distributions.  Also, because our corpus is organized
according to linguistic principles, it learns transfer rules that are
extendable by human system developers.


--------------------------------------\\

S::S : [NP PP VP] -> [VP NP PP] 
; Source and target production rules 
; (`constituent sequences')
(; Constituent alignments
  (X1::Y2)
  (X2::Y3)
  (X3::Y1)
  ; Analysis unification equations (`x-side constraints')
  ((x0 subj) == x1)
  (x0 = x3)
  ; Transfer Equations (`xy-constraints')
  (y0 = x0)
  ; Feature completion and Generation Constraints 
  ; (`y-side constraints')
  (y2 == (y0 subj))
  (y1 = y0)
  ((y0 agr) = (y1 agr))
  ((y1 agr) = (y2 agr)))


\begin{it}
\begin{small}
\begin{tabbing}
Good-ACC-SG \= day-ACC \kill \\
Hello. \\
Guten \> Tag. \\
Good-ACC-SG \> day-ACC \\

How \= go-3SG-PRES \= it \= you-DAT \kill \\
How are you? \\
Wie \> geht \> es \> dir? \\
How \> go-3SG-PRES \> it \> you-DAT \\ 

How-much \= cost-3SG-PRES \= this-NOM \kill \\
How much is this? (asking for price) \\
Wieviel \> kostet \> das? \\
How-much \> cost-3SG-PRES \> this-NOM \\

It-NOM \= is \= nice \= you-POLITE-ACC \= meet-INF \kill \\
It is nice to meet you. \\
Es \> ist \> nett\,  \> Sie \> kennen zu lernen. \\
It-NOM \> is \> nice \> you-POLITE-ACC \> meet-INF 
\end{tabbing}
\end{small}
\end{it}

For instance, many languages have more tenses than English. Without
knowing which verb tenses a given target language has, it is a hard
task to obtain all the different verb forms with an English-based
elicitation. This situation is very likely to arise when eliciting
form new languages we know very little about, specially with languages
that are very different from the ones that have been widely studied
before, such as Mapudungun, a polysynthetic, highly inflected
language. The following examples show how to conjugate the verb `seek'
(`kintun'), depending on who the actor is (that is, who is performing
the action):

\begin{it}
\begin{small}
\begin{tabbing}
i\~nche kintun (I seek)\\
eymi kintuymi (you seek)\\
fey kintuy (he/she/it seeks)\\
i\~nchi\~n kintui\~n (we all seek)\\
inchiu kintuyu (we two seek)\\
eym\"un kintuym\"un (you all seek)\\
eymu kintuymu (you two seek)\\
feyeng\"un kintuyng\"un (they all seek)\\
feyengu kintuyngu (they two seek)
\end{tabbing}
\end{small}
\end{it}

(slightly adapted from \cite{Calendino2001})


