Statistical models for unsupervised, semi-supervised and supervised transliteration mining

We present a generative model that efficiently mines transliteration pairs in a consistent fashion in three different settings, unsupervised, semi-supervised and supervised transliteration mining. The model interpolates two sub-models, one for the generation of transliteration pairs and one for the generation of non-transliteration pairs (i.e. noise). The model is trained on noisy unlabelled data using the EM algorithm. During training the transliteration sub-model learns to generate transliteration pairs while the fixed non-transliteration model generates the noise pairs. After training, the unlabelled data is disambiguated based on the posterior probabilities of the two submodels. We evaluate our transliteration mining system on data from a transliteration mining shared task and on parallel corpora. For three out of four language pairs, our system outperforms all semi-supervised and supervised systems that participated in the NEWS 2010 shared task. On word pairs extracted from parallel corpora with less than 2% transliteration pairs, our system achieves up to 86.7% F-measure with 77.9% precision and 97.8% recall.