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%\author{Katharina Probst}
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\section{Controlled Elicitation vs. Learning from Uncontrolled Corpora?}

In our work, we are interested in what we call a
`re\-source\--rich/re\-source\--poor situation': for one of the
languages in the translation pair, the resource-rich language,
linguistic and language-technological tools are easily available.
Such tools include monolingual dictionaries, part-\-of-\-speech
taggers, morphological analyzers, parsers and grammars. For the other
language, such resources are not available, or only to a limited
extent. This situation is not uncommon: while extensive language
technologies are available for languages such as English, Spanish, and
Japanese, other languages such as Croatian, Bulgarian, or indigenous
languages have been neglected.  Thus, when translating between a
resource-rich language such as English and a resource-poor language
such as Mapudungun (an indigenous language spoken in Southern Chile
and Argentina \cite{LevinEtAl2002}), dealing with the imbalance
between the information available on each language side becomes the
highest priority.

The decision of how to build an MT system in such a situation will
depend on the training data that is available. In particular, if a
large bilingual corpus is available, one could use it to infer
statistics, example-bases, or, as in our approach, transfer rules.
If no such corpus is available (a common case for unusual language
pairs like Spanish-Mapudungun), we can elicit a small, controlled
corpus and use it as structured training data.  Both forms of training
data have their advantages and disadvantages.  In the above sections, we have already described how
learning is facilitated in our system if the training data is a small,
but structured, corpus. Ideally, our system will be trained on both
unstructured and structured data.  The two types of data are
sufficiently different, so that we hope that the learned rules will
supplement each other.

%%------------------------------\\
%%\textit{In favor of large, uncontrolled, bilingual corpus:}\\

The existence of a large bilingual corpus brings about a number of
advantages.  One appeal of an unstructured training corpus is that no
elicitation corpus needs to be designed if it does not yet exist.  The
design of a corpus that covers major typological phenomena is a
non-trivial and time-consuming task.  However, once the elicitation
corpus is designed, it can easily be applied to new language pairs.

Learning from a large, unstructured training corpus has the further
advantage that quantitative statements about the language can be made.  The elicitation
corpus aims at covering all kinds of linguistic phenomena, while
keeping the corpus small.  Therefore, each single phenomenon does not
receive attention beyond a few sentences.  If one of the sentences
provides an exception to a rule, the system will learn two different
transfer rules, but it has no way of knowing which rule should be the
default.  In other words, the design of the elicitation corpus
inherently violates the distribution of rules in a given language
pair.  If learning from a large corpus, the distribution is preserved,
and can possibly be extracted and used by the learning algorithm.

%%------------------------------\\
%%\textit{In favor of controlled corpus:}\\

In the absence of a large bilingual corpus, we have chosen to obtain
training data via controlled elicitation. This means that we obtain
translated text from a bilingual user -- however, we carefully
construct the sentences that the user will translate in order to
optimize the training data for our system. In particular, the corpus
can aim at covering major typological phenomena or even at being
typologically complete.  Any unstructured training corpus will lack examples of various linguistic phenomena, with the problem becoming more prevalent the smaller the corpus is.  This serious data
sparseness problem can be overcome with a linguistically inspired
corpus.

The elicitation corpus is further designed to be applicable for any
language. This makes it a reusable resource that facilitates the quick
ramp-up of an MT system for a new language pair, given that one of the
languages is a language that the elicitation corpus is available
in. (This is in line with our assumption of a
resource-rich/resource-poor situation).  Not only can we reuse the
sentences and phrases that the corpus consists of, we can also parse
and disambiguate them once and then reuse this information for any new
resource-poor language.  The above technique results in time savings
for porting to a new language pair.  Furthermore, the information that
was encoded in the corpus on the resource-rich language side is less
noisy than if it was automatically obtained (such as by parsing
unstructured training data).  As was said above, we parse all training
elements in the corpus once and hand-disambiguate them, so that the
system has access to the correct parse.  We also hand-encode into
the corpus the type of the training example (e.g. NP, S).  While this
is a time-intensive operation, it again creates reusable information,
so that for new language pairs the work does not have to repeated and
the system has access to (relatively) noise-free data.

In creating an elicitation corpus, we have the opportunity to design
it to 1) fit the learning task at hand, SVSL in our case, and 2) use
control strategies in order to extract as much information from as
little training data as possible.

In order to fit the SVSL learning task, we have designed the
elicitation corpus compositionally.  As was described above, the
learning algorithms aim at learning rules that are built from rules
for smaller structures. Thus, it makes sense to first elicit simple
structures (such as noun phrases) and learn rules for them.  Later, we
elicit more complex structures (such as complex sentences that contain
a number of noun phrases) and learn compositional rules that take
advantage of the learned NP rules.

The corpus is also designed to be consistent with a control strategy
that allows the bilingual informant to navigate through it
efficiently.  Basic linguistic features and phenomena are elicited
first, followed by less  basic features.  For instance, the corpus
covers noun phrases and simple intransitive and transitive sentences
first, whereas relative clauses are not elicited until later.
Therefore, the ratio between the size of the training corpus and the
linguistic coverage of the system can be much smaller than when
learning from unstructured training data. This design also implies
that the time of the bilingual informant can be used most effectively:
even if he or she only translates part of the corpus, the resulting
learned rules cover common linguistic features and constructions.  The
more data translated, the more sophisitcated the resulting transfer
grammar will become.

Another way in which a controlled corpus can be useful is that it uses
knowledge of language typology to determine what needs to be learned
and what does not apply to a given language. This process was described briefly above as feature detection. For example, once we
determine that a language does not mark plural number on nouns, we do
not need to check whether it marks dual number.  Conversely, if we
know that a language encodes plural differently than dual, the system
will not attempt to learn a transfer rule that fits both dual and
plural.  It will not consider it noise if the predicted transfer rule
for a dual noun phrase does not match a transfer rule for plural that
has already been learned. 

%% ------------------------------\\

To summarize, both learning from an unstructured and a structured
corpus have their advantages.  Our system will adapt to a given
situation, depending simply on the availability of large amounts of
sentence-aligned bitext versus the availability of a native informant
who can translate the elicitation corpus.  Ideally, of course, both
types of training data can be combined in order to get the `best of
both worlds'. Currently, we have not yet developed sophisticated
techniques to apply our rule learning algorithm to unstructured text.
However, future versions of our system will provide this technology.

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