Marker-based Chunking for Analogy-based Translation of Chunks

An example-based machine translation (EBMT) system based on analogies requires numerous analogies between linguistic units to work properly. Consequently, long sentences cannot be handled directly in such a framework. In this paper, we inspect the quality of translation of chunks obtained by marker-based chunking in English and French in both directions. Our results show that more than three quarters of the chunks can be translated by the one-step analogy-based translation method, and that a little bit less than half of the chunks has at least one translation that matches exactly with one of the references.