Automatic extraction of translations from web-based bilingual materials
This paper describes the framework of the StatCan Daily Translation Extraction System (SDTES), a computer system that maps and compares web-based translation texts of Statistics Canada (StatCan) news releases in the StatCan publication The Daily. The goal is to extract translations for translation memory systems, for translation terminology building, for cross-language information retrieval and for corpus-based machine translation systems. Three years of officially published statistical news release texts at http://www.statcan.ca were collected to compose the StatCan Daily data bank. The English and French texts in this collection were roughly aligned using the Gale-Church statistical algorithm. After this, boundary markers of text segments and paragraphs were adjusted and the Gale-Church algorithm was run a second time for a more fine-grained text segment alignment. To detect misaligned areas of texts and to prevent mismatched translation pairs from being selected, key textual and structural properties of the mapped texts were automatically identified and used as anchoring features for comparison and misalignment detection. The proposed method has been tested with web-based bilingual materials from five other Canadian government websites. Results show that the SDTES model is very efficient in extracting translations from published government texts, and very accurate in identifying mismatched translations. With parameters tuned, the text-mapping part can be used to align corpus data collected from official government websites; and the text-comparing component can be applied in prepublication translation quality control and in evaluating the results of statistical machine translation systems.
|Keywords||Automatic translation extraction, Bitext mapping, Machine translation, Parallel alignment, Translation memory system|
Zhu, Q. (Qibo), Inkpen, D. (Diana), & Asudeh, A. (2007). Automatic extraction of translations from web-based bilingual materials. Machine Translation, 21(3), 139–163. doi:10.1007/s10590-008-9040-7