Low-density languages raise difficulties for standard approaches to natural language processing that depend on large online corpora. Using Persian as a case study, we propose a novel method for bootstrapping MT capability for a low-density language in the case where it relates to a higher density variant. Tajiki Persian is a low-density language that uses the Cyrillic alphabet, while Iranian Persian (Farsi) is written in an extended version of the Arabic script and has many computational resources available. Despite the orthographic differences, the two languages have literary written forms that are almost identical. The paper describes the development of a comprehensive finite-state transducer that converts Tajik text to Farsi script and runs the resulting transliterated document through an existing Persian-to-English MT system. Due to divergences that arise in mapping the two writing systems and phonological and lexical distinctions, the system uses contextual cues (such as the position of a phoneme in a word) as well as available Farsi resources (such as a morphological analyzer to deal with differences in the affixal structures and a lexicon to disambiguate the analyses) to control the potential combinatorial explosion. The results point to a valuable strategy for the rapid prototyping of MT packages for languages of similar uneven density.
Rapid Development of Translation Tools: Application to Persian and Turkish
Jan W. Amtrup | Karine Megerdoomian | Remi Zajac
COLING 2000 Volume 2: The 18th International Conference on Computational Linguistics
The Computing Research Laboratory is currently developing technologies that allow rapid deployment of automatic translation capabilities. These technologies are designed to handle low-density languages for which resources, be that human informants or data in electronically readable form, are scarce. All tools are built in an incremental fashion, such that some simple tools (a bilingual dictionary or a glosser) can be delivered early in the development to support initial analysis tasks. More complex applications can be fielded in successive functional versions. The technology we demonstrate has first been applied to Persian-English machine translation within the Shiraz project and is currently extended to cover languages such as Arabic, Japanese, Korean and others.