One of the limitations of translation memory systems is that the smallest translation units currently accessible are aligned
sentential pairs. We propose an example-based machine translation system which uses a ‘phrasal lexicon’ in addition to the
aligned sentences in its database. These phrases are extracted from the Penn Treebank using the Marker Hypothesis as a constraint
on segmentation. They are then translated by three on-line machine translation (MT) systems, and a number of linguistic resources
are automatically constructed which are used in the translation of new input.
We perform two experiments on testsets of sentences and noun phrases to demonstrate the effectiveness of our system. In so
doing, we obtain insights into the strengths and weaknesses of the selected on-line MT systems. Finally, like many example-based
machine translation systems, our approach also suffers from the problem of ‘boundary friction’. Where the quality of resulting
translations is compromised as a result, we use a novel, post hoc validation procedure via the World Wide Web to correct imperfect translations prior to their being output to the user.