A generic framework for Arabic to English machine ... - Acsu Buffalo

4.3.5 Statistical machine translation
4.4. LINGUISTIC ASPECTS OF MT
The ideas behind statistical machine translation come out of information theory. Essen-
tially, the document is translated on the probability p(e|a) that a string e in the target
language (for example English) is the translation of a string a in the source language (for
example Arabic). As translation systems are not able to store all native strings and their
translations, a document is typically translated sentence by sentence, but even this is not
enough. We assign to every pair of strings (e|a) a number P(e|a), which we interpret as
the probability that a translator, when presented with e, will produce a as its translation.
You could imagine another program that takes a sentence a as input, and outputs every
conceivable string e along with its P(e|a). This program would take a long time to run,
even if you limit English translations to some arbitrary length. They seek the English sen-
tence e that maximizes P(e|a) and minimizes time (Brown et al. 1993). To summarize,
we compute P(e|a) by summing the probabilities of all alignments. For each alignment,
we make two significant simplifying assumptions: Each English word is generated by
exactly one Arabic word; and the generation of each English word is independent of the
generation of all other English words in the sentence. This is clearly not true in theory.
4.4 Linguistic aspects of MT
In this section we will look more closely at the kinds of linguistic problems that MT has to
face and will discuss ways in which MT programs work around these problems. We will
distinguish monolingual problems of morphology, lexical ambiguity, syntactic ambiguity,
pragmatic aspects from bilingual problems of language contrast: lexical mismatches,
structural divergence, typological differences.
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