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\title{Rewrite of Discussion Section}
\author{Katharina Probst}
\date{\today}

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\section{Controlled Elicitation or Learning from Uncontrolled Corpora?}

In our work, we are interested in what we call a `resource-rich/resource-poor situation'.  In other words, NLP resources such as a parser and a grammar are available for one of the translation languages, whereas they are not necessarily obtainable for the other language. If we classify learning situations by this criterion, there is still the question of whether a large uncontrolled corpus is available.  Let us assume for sake of argument that this is not the case.  We will emphasize the strenghts of our approach in this, assumed, situation.  It will then become clear that even if a large uncontrolled corpus is available, our approach can be a valid contribution to learning from the large corpus.

The situation where no large bilingual corpus is available is not uncommon, in particular when one of the languages is a minority language such as Mapudungun.  If we still wish to build an MT system to translate between the languages, we can make a number of choices.  In our approach, a bilingual user translates a set of carefully constructed sentences and phrases, what we call the elicitation corpus.  This brings about a number of advantages.  For instance, constructing an elicitation corpus allows us to control the typologicial and grammatical variety of the sentences and phrases. 


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If 3 exists:\\

One appeal of a large, unstructured training corpus is that no elicitation corpus needs to be designed.  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 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.

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\textit{Note: where does the following go??\/}
Two points should be made clear from the start: first, our learning approach is applicable equally to uncontrolled and controlled corpora.  However, as we will argue, learning is facilitated if a small but controlled corpus is used as training data. Second, 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.



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