Transliteration alignment

This paper studies transliteration align-ment, its evaluation metrics and applica-tions. We propose a new evaluation met-ric, alignment entropy, grounded on the information theory, to evaluate the align-ment quality without the need for the gold standard reference and compare the metric with F-score. We study the use of phono-logical features and affinity statistics for transliteration alignment at phoneme and grapheme levels. The experiments show that better alignment consistently leads to more accurate transliteration. In transliter-ation modeling application, we achieve a mean reciprocal rate (MRR) of 0.773 on Xinhua personal name corpus, a signifi-cant improvement over other reported re-sults on the same corpus. In transliteration validation application, we achieve 4.48% equal error rate on a large LDC corpus.