We propose a bootstrapping approach to training a memoriless stochastic transducer for the task of extracting transliterations from an English-Arabic bitext. The transducer learns its similarity metric from the data in the bitext, and thus can function directly on strings written in different writing scripts without any additional language knowledge. We show that this bootstrapped transducer performs as well or better than a model designed specifically to detect Arabic-English transliterations.
REF proposed the use of memoryless stochastic transducer for extracting transliteration through word similarity metrics.
Bootstrapping a Stochastic Transducer for Arabic-English Transliteration Extraction
45th Annual Meeting of the Association of Computational Linguistics
Computer Science