Feature-Based Decipherment for Machine Translation

Iftekhar Naim, Parker Riley, Daniel Gildea


Abstract
Orthographic similarities across languages provide a strong signal for unsupervised probabilistic transduction (decipherment) for closely related language pairs. The existing decipherment models, however, are not well suited for exploiting these orthographic similarities. We propose a log-linear model with latent variables that incorporates orthographic similarity features. Maximum likelihood training is computationally expensive for the proposed log-linear model. To address this challenge, we perform approximate inference via Markov chain Monte Carlo sampling and contrastive divergence. Our results show that the proposed log-linear model with contrastive divergence outperforms the existing generative decipherment models by exploiting the orthographic features. The model both scales to large vocabularies and preserves accuracy in low- and no-resource contexts.
Anthology ID:
J18-3006
Volume:
Computational Linguistics, Volume 44, Issue 3 - September 2018
Month:
September
Year:
2018
Address:
Venue:
CL
SIG:
Publisher:
Note:
Pages:
525–546
Language:
URL:
https://www.aclweb.org/anthology/J18-3006
DOI:
10.1162/coli_a_00326
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/J18-3006.pdf