A Language-Neutral Sparse-Data Algorithm for Extracting Translation Patterns

In this paper, we present an algorithm for the automatic extraction of translation patterns between two (Indo-)European languages. These consist of possibly discontiguous text fragments, with the bilingual relationship between the text fragments and the discontinuities between them made explicit. The patterns are extracted from a bilingual parallel corpus aligned at the sentence level, without the need for linguistic analysis, and are used to build a translation memory database which is intended for use in a machine aided human translation (MAHT) setting, such as a translator’s workbench (TWB). The patterns extracted could also form the basis for example-based machine translation (EBMT) without the need for complex linguistic or statistical processing. Given a TM database made up of our concept of translation patterns and a SL input string, relevant translation patterns combine to form TL translations as suggestions to the translator. We evaluate the accuracy of the translation patterns extracted along with the quality of translations produced.