Statistical Models for Unsupervised, Semi-Supervised Supervised Transliteration Mining

Hassan Sajjad, Helmut Schmid, Alexander Fraser, Hinrich Schütze


Abstract
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 unlabeled data using the EM algorithm. During training the transliteration sub-model learns to generate transliteration pairs and the fixed non-transliteration model generates the noise pairs. After training, the unlabeled data is disambiguated based on the posterior probabilities of the two sub-models. 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 fewer than 2% transliteration pairs, our system achieves up to 86.7% F-measure with 77.9% precision and 97.8% recall.
Anthology ID:
J17-2003
Volume:
Computational Linguistics, Volume 43, Issue 2 - June 2017
Month:
June
Year:
2017
Address:
Venue:
CL
SIG:
Publisher:
Note:
Pages:
349–375
Language:
URL:
https://www.aclweb.org/anthology/J17-2003
DOI:
10.1162/COLI_a_00286
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/J17-2003.pdf